Issue 
A&A
Volume 598, February 2017



Article Number  A2  
Number of page(s)  32  
Section  Interstellar and circumstellar matter  
DOI  https://doi.org/10.1051/00046361/201424287  
Published online  24 January 2017 
Modelling clumpy photondominated regions in 3D
Understanding the Orion Bar stratification
I. Physikalisches Institut, Universität zu Köln, Zülpicher Straße 77, 50937 Köln, Germany
email: sandree@ph1.unikoeln.de
Received: 27 May 2014
Accepted: 16 September 2016
Context. Models of photondominated regions (PDRs) still fail to fully reproduce some of the observed properties. In particular they do not reproduce the combination of the intensities of different PDR cooling lines together with the chemical stratification, as observed for example for the Orion Bar PDR.
Aims. We aim to construct a numerical PDR model, KOSMAτ 3D, to simulate full spectral cubes of line emission from arbitrary PDRs in three dimensions (3D). The model will reproduce the intensity of the main cooling lines from the Orion Bar PDR and the observed layered structure of the different transitions.
Methods. We built up a 3D compound, made of voxels (3D pixels) that contain a discrete mass distribution of spherical “clumpy” structures, approximating the fractal ISM. To analyse each individual clump the new code was combined with the KOSMAτ PDR model. Probabilistic algorithms were used to calculate the local FUV flux for each voxel as well as the voxelaveraged line emissivities and optical depths, based on the properties of the individual clumps. Finally, the computation of the radiative transfer through the compound provided full spectral cubes. To test the new model we tried to simulate the structure of the Orion Bar PDR and compared the results to observations from HIFI/Herschel and from the Caltech Submillimetre Observatory (CSO). In this context new Herschel data from the HEXOS guaranteedtime key program is presented.
Results. Our model is able to reproduce the lineintegrated intensities within a factor of 2.5 and the observed stratification pattern within 0.016 pc for the [Cii] 158 μm and different ^{12/13}CO and HCO^{+} transitions, based on the representation of the Orion Bar PDR by a clumpy edgeon cavity wall. In the cavity wall, a large fraction of the total mass needs to be contained in clumps. The mass of the interclump medium is constrained by the FUV penetration. Furthermore, the stratification profile cannot be reproduced by a model that has the same amount of clump and interclump mass in each voxel; dense clumps need to be removed from the PDR surface.
Key words: photondominated region / ISM: structure / ISM: clouds / submillimeter: ISM / infrared: ISM / radiative transfer
© ESO, 2017
1. Introduction
Stars form from the interstellar medium (ISM), in its dense and cold regions, inside molecular clouds. Hence, a better understanding of the chemical and physical processes taking place in molecular clouds, their internal structure, and the interaction between molecular clouds and the interstellar radiation field is an important step to constrain our knowledge on star formation processes.
The energy which heats the different components of the ISM can originate from different sources, for instance from cosmic rays, from the dissipation of (magnetised) turbulence, or from the interstellar radiation field (including radiation from nearby stars). In photondominated (or photodissociation) regions (PDRs) the dominating energy input is provided by the interstellar radiation field. More precisely, a PDR is a region in interstellar space where the photon energies fall below the ionisation energy of hydrogen, but where the interstellar farUV (FUV) radiation field still dominates the heating processes and the chemistry of the ISM (photon energies: 6 eV < hν < 13.6 eV). Here, the lower threshold of 6 eV is an estimate of the work function of a typical interstellar dust grain. The estimate of the work function varies in literature. 6 eV are stated in de Jong et al. (1980), more recent works discuss examples with work functions of 5 eV and of 7 eV (Hollenbach & Tielens 1999), Weingartner & Draine (2001b) adopt 4.4 eV for graphite grains and 8 eV for silicates. Cooling of the gas is dominated by fine structure line emission by atoms and ions, especially [Oi] 63 μm and 145 μm, [Cii] 158 μm and [Ci] 609 μm and 370 μm, by H_{2} rovibrational, and by molecular rotational lines (mainly CO) (Tielens & Hollenbach 1985; Hollenbach & Tielens 1997). Farinfrared (farIR) continuum emission by dust grains and the emission features of polycyclic aromatic hydrocarbons (PAHs) are observed. At high densities gas and dust are tightly coupled via collisions and the IR emission of the dust grains can contribute to the cooling of the gas. As PDR emission dominates the IR and submillimetre spectra of star forming regions and galaxies (Röllig et al. 2007) they are the subject of many observations and extensive modelling. Photondominated regions can be found in many different astrophysical scenarios, however, here we focus on the transition zone between Hii and molecular regions illuminated by the strong FUV radiation from young stars.
Many different PDR models have been developed aiming to relate the observed line and continuum emission to the physical parameters of the emitting region and to understand the physical processes taking place in PDRs (e.g. Tielens & Hollenbach 1985; Sternberg & Dalgarno 1989; Koester et al. 1994). The models focus on different key aspects and exploit different geometries. An overview, emphasizing advantages and disadvantages of the different PDR models, can be found in the comparison study by Röllig et al. (2007). Since then, most of the codes have been significantly improved (see e.g. Röllig et al. 2013; Le Bourlot et al. 2012; Ferland et al. 2013). A major new step was provided by the extension to fully threedimensional configurations, which allows for the modelling of PDRs with arbitrary geometries, by Bisbas et al. (2012).
In the molecular clouds the FUV field is attenuated, mainly due to absorption by dust grains. The decreasing FUV field strength causes a layered structure of different chemical transitions, referred to as chemical stratification. Chemical stratification can be observed in many different PDRs and within different scenarios, for instance in the Hii region and molecular cloud M17 (Stutzki et al. 1988; Pellegrini et al. 2007; PérezBeaupuits et al. 2012), the Horsehead Nebula (Pety et al. 2007), planetary nebulae (for example NGC 7027, see Graham et al. 1993) or within protoplanetary disks (see for instance Kamp et al. 2010). Furthermore, it is observed in the Orion Bar PDR as discussed in Sect. 3.
In other PDRs we find a spatial coexistence of different PDR tracers that can be explained by a clumpy or filamentary cloud structure (Stutzki et al. 1988; Stutzki & Guesten 1990; Howe et al. 1991). Actually, most observations of molecular clouds show filamentary, turbulent structures and substructures on all scales observed so far. Such clouds can be described by fractal scaling laws. Fractal structures contain surfaces everywhere throughout the cloud, hence, a large fraction of the molecular material is located close to a surface. Combined with a low volume filling factor (VFF) of the dense condensations this implies that surfaces inside the clouds are exposed to the interstellar radiation field and form PDRs (Burton et al. 1990; Ossenkopf et al. 2007).
Several attempts have been made to model the 3D and inhomogeneous structure of PDR gas. For instance, Stutzki et al. (1998) have proven that the fractal properties can be mimicked by an ensemble of clumps with an appropriate mass spectrum. Based on this approach Cubick et al. (2008) show that an ensemble of such clumps, immersed in a thin interclump medium, can be used to simulate the large scale fine structure emission from the Milky Way. More recently, Glover et al. (2010) developed 3D simulations of the turbulent interstellar gas with coupled thermal, chemical and dynamical evolution. Levrier et al. (2012) use the Meudon PDR code to compare the chemical abundances in a homogeneous cloud to the chemical abundances in a cloud with density fluctuations.
However, a distribution of spherical clumps of different sizes that enables modelling of arbitrary 3D geometries has not yet been described. For the Orion Bar PDR, one of the most prominent PDRs in the solar neighbourhood, a match between observations and simulation results of the highJ CO line intensities, combined with the observed stratification profile is still pending. Planeparallel PDR models fail in this context, because a match of the highJ CO line intensities always requires high densities which imply a very sharp and dense PDR structure that is not consistent with the observed stratification covering, in the case of the Orion Bar PDR, at least 0.03 pc (see for example Pellegrini et al. 2009, or the data presented in this work, Sect. 3.2). For example in Röllig et al. (2007, their Fig. 11) the C^{+}toCtoCO transition has been simulated using many different PDR codes (for a gas density of 10^{5.5} cm^{3} and an FUV field strength of 10^{5} times the mean interstellar radiation field Draine 1978). For all models the transition takes place at optical depths A_{V} ≲ 4 and using A_{V}/N_{H} = 6.289 × 10^{22} cm^{2} (Röllig et al. 2007) we find that the stratified layers do not cover more than 0.0065 pc. More sophisticated models are necessary to reproduce the observed line intensities as well as the observed chemical stratification. To overcome this deficiency we have set up an extension of the KOSMAτ PDR code, denoted KOSMAτ 3D, which enables us to model clumpy PDRs in 3D. The code supports a spatial variation of PDR parameters, like the mean density, the clumpsize distribution, or the strength of the impinging FUV field. Furthermore, to exploit the copious information contained in observed line profiles, the new code analyses a region at arbitrary velocities and hence the simulations of full line profiles.
In Sect. 2 we discuss the extension of the KOSMAτ PDR model to a clumpy 3D PDR model. To test the new code we use selected observations of the Orion Bar PDR which are presented in Sect. 3. The 3D model of the Orion Bar PDR is discussed in Sect. 4. In Sect. 5 we present the fitting process: first we discuss the parameters that are varied within our model setup and define functions of merit that are used for the evaluation of different models. We do then present the simulation outcome for many different models and provide a discussion. The results are summarised in Sect. 6.
2. 3D PDR modelling
In this section we discuss the extension of the KOSMAτ PDR model to a clumpy 3D PDR model. First the properties of the KOSMAτ PDR model are summarised and modelling of the inhomogeneous ISM based on fractal structures is discussed. Afterwards, the 3D model setup is described including all steps which are necessary to simulate maps and spectra, comparable to astronomical observations.
2.1. The KOSMAτ PDR model
The KOSMAτ PDR model^{1} (Röllig et al. 2006) has been developed at the University of Cologne in collaboration with the TelAviv University. Contrary to many other models (see Röllig et al. 2007, and references therein), which are based on planeparallel geometries, the KOSMAτ model utilises a spherical geometry, clumps, to model the structure of a PDR.
A single clump is parameterised by its total hydrogen mass M_{cl}, the surface hydrogen density n_{s} = n_{H,s} + 2 n_{H2,s} and the strength of the incident FUV field. The FUV flux is assumed to be isotropic (see discussion in Sect. 5.4.5) and is measured in units of the Draine field integrated over the FUV range (χ_{0} = 2.7 × 10^{3} erg s^{1} cm^{2}, Draine 1978). In addition, the model accounts for cosmic ray primary ionisations at a constant rate. In this work a rate of 2 × 10^{16} s^{1} per H_{2} molecule is used (Hollenbach et al. 2012). In the model the radial density distribution n(r) of the clumps is divided into a core and an outer region: (1)where R_{cl} is the radius of the clump and xR_{cl} with x ∈ [0,1] is the radius of the clump core. The exponent, a, and the size of the core are input parameters of the KOSMAτ code, a = 0 and x ≠ 0 for example enforces a constant density sphere. In many studies (Stoerzer et al. 1996; Cubick et al. 2008) and also in this work, a = 1.5 and x = 0.2 are chosen with the aim to generate clumps that approximate BonnorEbert spheres^{2}. Consequently, the averaged density of one clump is given by (2)To analyse such a clump the frequency dependent mean (averaged over the full solid angle) FUV intensity is derived at different positions between clump centre and clump surface (for different radii r ∈ [0,R_{cl}]). This calculation is based on the multi component dust radiative transfer (MCDRT) code (see Yorke 1980; Szczerba et al. 1997; Röllig et al. 2013) and includes isotropic scattering. The same code accounts for the IR continuum radiative transfer inside the clump. The KOSMAτ PDR code includes H_{2} selfshielding based on the results from Draine & Bertoldi (1996), furthermore, CO photodissociation is computed based on Visser et al. (2009).
To derive the physical conditions and the chemical composition of the clump, the KOSMAτ code iteratively solves the following steps: the chemical network, which can be assembled from a modular chemical network (Röllig et al. 2013), is used to derive local abundances, based on the local conditions. Using lineofsightintegrated escape probabilities for the main cooling lines the local energy balance, that is heating and cooling processes are evaluated in steady state. After sufficient iterations (when a predefined convergency criterion is met; here: when the calculated column densities vary less than 1% between subsequent iterations), ray tracing through the clump is solved for lines of sight at different impact parameters p from the centre point of the spherical clump. For details on the ONION radiative transfer model see Gierens et al. (1992). The emission from the spherical clumps is not sensitive to their internal density structure, but fully parametrised by their surface density. A parameter study by Mertens (2013) showed that modifications of the internal density profile hardly change the chemical abundance profiles as long as the surface density is kept constant.
In the first part of the KOSMAτ 3D PDR code the averaged attenuation of the FUV flux caused by clumps with different masses and densities is needed. The second part of the code uses clumpaveraged line intensities and optical depths of atomic and molecular transitions. To derive the averaged FUV attenuation of a clump we calculate the hydrogen column density along a line of sight through the clump, depending on the impact parameter, that is (3)where n(...) is the density profile as given by Eq. (1). N_{H}(p) can be used to calculate the attenuation in the FUV range, τ_{j, FUV}(p)^{3}, assuming that both quantities are proportional to each other (see Sect. 2.3.2). Furthermore, the attenuation of line intensities is proportional to the factor exp(−τ(p)) (see for example Eq. (B.3)), where τ(p) denotes the optical depth for a line of sight with impact parameter p. Therefore, the factor exp(−τ(p)) needs to be averaged over the projected surface of the clump, meaning that for each clump we numerically solve the integral (4)The line intensities I_{j, line}(p) of different atomic and molecular transitions have been averaged correspondingly, that is (5)and the optical depths of the different transitions, τ_{j, line}(p), are processed analogously to Eq. (4). The , and have been derived on a parameter grid of surface densities, clump masses and impinging FUV fluxes. The KOSMAτ 3D code introduced in this work imports such a model grid and, if necessary, interpolates between gridpoints to derive the intensities and optical depths needed in the simulations. Details on the grid used for the presented Orion Bar simulations are summarised in Table 1.
Overview of the most important model parameter.
2.2. Modelling the fractal ISM
The fractal structure of molecular clouds can be mimicked by a superposition of spherical clumps following a welldefined clumpmass spectrum, building up a clumpy ensemble (Stutzki et al. 1998; Cubick et al. 2008). The clumpmass spectrum can be described by a powerlaw (6)giving the number of clumps dN_{cl} in the mass bin dM_{cl}. In addition the masses of the clumps are related to their radii R_{cl} by the masssize relation (7)The powerlaw exponents α and γ have been subject to many studies. Kramer et al. (1998) present clump mass spectra, derived using the squarefitting procedure gaussclump (Stutzki & Guesten 1990), of seven different molecular clouds, covering a wide range of physical properties and cloud sizes. They test and discuss the reliability of the mass spectra by studying the dependence on the control parameter of the decomposition algorithm. For all clouds from their sample they find that α lies between 1.6 and 1.8 implying that small clumps are more numerous. No turnover of the powerlaw index is observed especially not for small, gravitationally unbound objects.
The powerlaw exponent γ has for instance been discussed by Elmegreen & Falgarone (1996). They analysed different cloud surveys from literature (based on different methods of clump identification) and find an exponent γ = 2.4−3.7 for single cloud surveys and an “allcloud slope” in the range 2.2−2.5. Hence, smaller clumps are expected to be denser. Using a second method they derive a fractal dimension D = 2.3 ± 0.3 for the same surveys which theoretically is expected to be equal to the exponent γ.
Heithausen et al. (1998) combined and analysed large and small scale data of the Polaris Flare to derive the power law slopes over a mass range of at least 5 orders of magnitude, from several ten solar masses, down to masses less than that of Jupiter (about 10^{3}M_{⊙}). Using the CO 1−0 and 2−1 lines they find α = 1.84 and γ = 2.31, values which are comparable to the ranges stated above and which we adapt for this work.
We note that in some more recent works (see review by Offner et al. 2014, and references therein) a turnover in the coremass function has been reported for lowmass clumps. Such a deviation from the powerlaw is not included in the KOSMAτ 3D PDR code. The influence of the very small clumps on the simulation outcome is investigated in Sect. 5.3.5.
2.2.1. Continuous description
Assuming that the masses of the clumps in an ensemble lie between a lower and an upper cutoff mass, m_{l} and m_{u}, one can derive the number of clumps N_{ens} in the ensemble (see Cubick et al. 2008): (8)and the total ensemble mass (9)relating the constant A to the ensemble mass. For the observed values of α below two an ensemble contains more lowmass than highmass clumps, still, the highmass clumps provide a larger fraction of the ensemble mass. The constant C in Eq. (7) depends on the averaged ensemble density ρ_{ens} and the cutoff masses: (10)
2.2.2. Discrete description
We use a discrete description for a simplified numerical treatment of the clumpy ensemble (see Cubick 2005). Here, the mass spectrum of the clumps is not continuous, but represented by clumps at discrete mass points { M_{j} } _{j = 1...nM}. We use a logarithmic parameter scale, in other words with B = 10. Indices are ordered with increasing masses. For the Orion Bar simulations we used M_{nM} = 1 M_{⊙} (Lis & Schilke 2003, see Sect. 4) whereas the simulations of the whole Milky Way by Cubick (2005) rather correspond to M_{nM} = 100 M_{⊙}. We assume that the number of clumps N_{j} with mass M_{j} is given by the power law (11)with a constant A_{d} (d = discrete) similar to Eq. (8). This yields for the total mass M_{J} of clumps with mass M_{j}(12)For each ensemble the total mass of the ensemble M_{ens} and the averaged ensemble density ρ_{ens} are input parameters which can be fixed if the physical parameters of the PDR are known or they can be used as fitting parameters otherwise. The total ensemble mass is given by M_{ens} = ∑ _{j}N_{j}M_{j}. Inserting N_{j} from Eq. (11) we find (13)The density of the individual clumps in the ensemble deviates from the ensemble averaged density ρ_{ens} according to the masssize relation (Eq. (7)), depending on their specific masses. For given mass points { M_{j} } _{j = 1...nM} the volumes { V_{j} } _{j = 1...nM} of individual clumps can be calculated using Eq. (7) which yields (14)and consequently the averaged density of a clump is found to be (15)The ensemble averaged density ρ_{ens} is equal to the total ensemble mass, divided by the total ensemble volume (16)and inserting Eqs. (11) and (14) we derive (17)Inserting Eq. (15) yields an expression for the density of clumps with mass M_{j} as a function of the average ensemble density, (18)In addition, the number of clumps N_{j} with mass M_{j}, as a function of the total ensemble mass, is found by combining Eqs. (11) and (13): (19)N_{j} as given by Eq. (19) and ρ_{j} given via Eq. (18) uniquely define the parameters of the overall ensemble.
In the 3D model the clumps of an ensemble are randomly distributed in a voxel (3D pixel) with a known volume Δs^{3} (see Sect. 2.3.3). The VFF, that is the fraction of the volume filled by clumps is given by (20)In principle for the discrete description artificial cutoff masses can be chosen in such a way that the parameters of the discrete description match those of the continuous description. However, one should note that it is not possible to conserve the total mass and the number of clumps within a mass interval when switching from the continuous to the discrete description (while using B = 10). Here, we have fixed the total ensemble mass which is assumed to be a known quantity. As the continuous description is not needed for the 3D PDR model, we will stick to the discrete description as an independent model.
2.3. Threedimensional setup
In irradiated molecular clouds we find position dependent conditions: the FUV field strength will decrease with increasing depth into the clouds due to extinction; furthermore, the average density and composition of the cloud may change. To model PDRs we set up a 3D model which can replicate arbitrary geometries using voxels. Each voxel contains at least one clumpy ensemble. Furthermore, for each mass point and for each voxel a velocity dispersion between the individual clumps is applied. The radiative transfer (Sect. 2.3.4) enables the simulation of lineintegrated maps as well as the modelling of full line profiles.
2.3.1. Ensemble statistics: area filling and clumps intersecting one line of sight
In the 3D setup each ensemble is contained in a 3D voxel having a projected surface area Δs^{2} perpendicular to the line of sight between the observer and the voxel. The shape of the projected surface is arbitrary, but the volume should be spanned by the product of this surface area with the voxel depth. For the presented algorithm for example a cuboid or a cylinder could be used and give the same results. A different viewing angle to the same geometry, therefore needs a resampling of the density structure into new voxels where the z axis is parallel to the line of sight. The clumps, building up the ensemble, are randomly positioned in the voxel resulting in a number surface density N_{j}/ Δs^{2} for each mass point j.
If we consider an arbitrary line of sight, perpendicular to the projected area Δs^{2}, through the ensemble, the probability distribution describing with how many randomly positioned clumps of mass M_{j} the line of sight intersects is given by the binomial distribution (21)where k_{j} is the number of clumps pierced by the line of sight, p_{j} is the probability that the line of sight intersects with a specific clump of mass M_{j} and N_{j} is the total number of clumps with mass M_{j}. The intersection probability p_{j} is given by .
In the following Sects. 2.3.2 and 2.3.3 binomial distributions (Eq. (21)) are used to calculate ensembleaveraged quantities, namely the ensembleaveraged FUV attenuation as well as ensembleaveraged line intensities and optical depths. As binomial distributions are discrete probability distributions, the numbers of clumps, N_{j}, need to be integer values. This is not automatically provided by Eq. (19), however, scaling of the surface size Δs^{2} (projected surface of the voxel = pixel) does not change the results for the ensembleaveraged quantities as long as the number surface density, N_{j}/ Δs^{2}, is kept constant for each mass point. Therefore, we rather consider a scaled “superpixel” of area (Δs′)^{2} = Δs^{2}·c with a constant c> 0. Consequently, the numbers of clumps, N_{j}, need to be scaled accordingly: N′_{j} = N_{j}·c. The constant c is chosen in a way that the following conditions are met:

a)
The projected clump areas of the largestclumps need to be smaller than a superpixel area:, in other words .

b)
, that is the number of clumps with mass M_{nM}, is always an integer value.

c)
is chosen to be the smallest value possible that does not contradict a) or b) to optimise for computing speed. This typically implies . In this work scaling to is only performed for the ensemble representing the dense clumps. For the interclump medium, which contains only one type of clumps, is chosen to be larger (typically ). For details see Appendix A.
After clump numbers and pixel surface area have been scaled the numbers of clumps, N′_{j ≠ nM}, are rounded to integer values. As the numbers of lowmass clumps are significantly higher then the number of highmass clumps, , due to the clumpmassspectrum (see Eq. (6)) these rounding errors are negligible.
In general, rounding errors can always be decreased by scaling to larger surface areas and consequently larger numbers of clumps. However, we found that the error made by rounding after step c) is already negligible and therefore optimised for computing speed. Furthermore, to increase the computing speed, the binomial distribution can be approximated by a normal distribution if the expected value μ_{j} = N_{j}p_{j} ≫ 1 and the number of clumps N_{j} → ∞. In the code this simplification is implemented for the case μ_{j}> 5 and N_{j}> 1000. However, in the presented Orion Bar setup we usually find μ_{j}< 5 due to the low number surface densities.
2.3.2. Voxel dependent FUV field strength
The line intensities and optical depths of the clumps contained in the voxels at different positions in the 3D model depend on the local FUV field strength. The FUV flux, coming from a known direction, is attenuated inside the PDR.
The FUV attenuation is proportional to the total hydrogen column density along the line of sight between the FUV source and the respective voxel. Röllig et al. (2013) discuss the FUV extinction in terms of the FUVtoV colour, k_{FUV} = ⟨ A(λ) /A(V) ⟩ _{λ}, where the averaging is performed over an energy range from 6 to 13.6 eV. The FUVtoV colour k_{FUV} is derived based on different grainsize distributions for the interstellar dust. For the model of the Orion Bar we adapt k_{FUV} ≈ 1.7 based on the grainsize distribution from Weingartner & Draine (2001a) for R_{V} = 5.5 (R_{V} = A_{V}/ (E(B−V)) is the ratio between visual extinction and reddening), the highest carbon abundance (for R_{V} = 5.5) in the smallgrain populations and a constant grain volume per hydrogen atom during their fitting process^{4}. R_{V} = 5.5 has been observed towards Θ^{1} Ori C and is potentially representative for very dense clouds (Draine & Bertoldi 1996). The high carbon abundance in small grains is justified by the observation of strong PAH features in the Orion Bar region (Pilleri et al. 2012). Combining k_{FUV} with the normalisation for the extinction curve A_{V}/N_{H} = 5.3 × 10^{22} cm^{2} from Weingartner & Draine (2001a), hydrogen column densities that have been derived for different lines of sight through individual clumps (see Eq. (3)) can be transformed into the related FUV attenuations.
For a given ensemble, let X be the set of all possible combinations of clumps intersecting one line of sight, that is X = { k_{j} } _{j = 1,...,nM} with 0 ≤ k_{j} ≤ N′_{j}. Consequently, one specific combination of clumps is described by an element x ∈ X, which is a set of n_{M} numbers that provide the number of clumps that intersect at each mass point j. For an element x the FUV attenuation along the line of sight is given by (22)The probability to find this combination of clumps intersecting the line of sight is the product of binomial distributions, Eq. (21), (23)In principle Eq. (23) has to be evaluated for each possible combination, however, some combinations are highly improbable and can be neglected within the algorithm to increase its computing speed. This is done by only accounting for numbers of clumps k_{j} which lie in a interval^{5} around the expected value of the respective binomial distribution. Calculations presented in this paper have been performed with n_{cut} = 3. In the presented simulations we found (24)confirming the low error of our approximation. Finally, the ensembleaveraged FUV attenuation can be derived using (25)where ⟨ ⟩ _{ens} denotes the ensembleaveraged value. The FUV attenuation in each voxel can then be described by the effective optical depth ⟨ τ_{FUV} ⟩ _{ens} = −ln( ⟨ e^{− τFUV} ⟩ _{ens}). If a voxels contains two ensembles to represent interclump medium and dense clumps, the algorithm presented above needs to be run for both ensembles separately. Finally, the ensembleaveraged contributions from clump and interclump medium are summed up, meaning that ⟨ τ_{FUV} ⟩ _{tot} = ⟨ τ_{FUV} ⟩ _{ens,cl} + ⟨ τ_{FUV} ⟩ _{ens,inter}.
2.3.3. Ensembleaveraged emissivities and opacities
Similar to the FUV attenuation, we need the ensembleaveraged line intensities and optical depths of each voxel to compute the emission of the PDR. Clump averaged line intensities and optical depths are provided by the KOSMAτ model (see Sect. 2.1). In principle, the ensembleaveraged emissivities and optical depths can be derived based on the same algorithm as the ensembleaveraged FUV attenuation in Sect. 2.3.2. However, there is one major difference: while FUV absorption is a continuum process, line absorption and emission is velocity dependent and hence we have to account for the intrinsic velocities of single clumps. An algorithm accounting for a velocity distribution between identical cloud fragments has already been discussed by Martin et al. (1984). The algorithm presented here is similar but capable to treat ensembles made of different clumps.
In the KOSMAτ 3D code the velocityspace is discretised into velocity bins of width Δv around the centre velocities v_{i} (i = 1,...,i_{max}) and the velocity of each clump from a bin v_{i} ± Δv/ 2 is approximated by v_{i}. Furthermore, we assume a Gaussian velocity distribution with standard deviation σ_{ens,j} for the clumps at mass point j, accounting for random motions of the single clumps. If N_{j} is the total number of clumps at mass point j then the number of clumps at mass point j whose centre velocity lies inside the bin around centre velocity v_{i} is given by (26)where v_{sys} denotes the systematic velocity of the whole region (or of the voxel). The same discussion as in Sect. 2.3.1 applies here, on other words it is necessary to ensure that the numbers of clumps ΔN_{j,i} are integer values which can be achieved by scaling of the pixel size. To convert the ΔN_{j,i} into integer values the algorithm presented in Sect. 2.3.1 is used. In general the scaling factor (factor c in Sect. 2.3.1) will be different for each velocity bin.
If we consider a specific emission line and an observing velocity v_{obs}^{6} we are interested in the contributions to the line intensity and optical depth provided by the clumps at all centre velocities { v_{i} } around velocity v_{obs}. To derive the contribution from the clumps at a single centre velocity v_{i}, we use (analogously to Sect. 2.3.2) combinations^{7} of clumps x_{i} ∈ X_{i} with X_{i} = { k_{j,i} } _{j = 1,...,nM} and with 0 ≤ k_{j,i} ≤ ΔN′_{j,i}. Furthermore, one can calculate the line intensities and optical depths of each combination x_{i}, which are given by where σ_{j, line} is the intrinsic line width (standard deviation) of a single clump at mass point j and and are the clumpaveraged (Eqs. (4) and (5)) linecentre (peak) intensities and optical depths computed by the KOSMAτ PDR code. In Eqs. (27) and (28) is would be more precise to average over the velocity bin, that is replace the exponential function by (29)However, we found that the effect of the integral is small. In a test run with 11 velocity bins with v_{i} = 6.3,7.3,...,16.3 we compared the calculated ensembleaveraged quantities with and without integrating over the exponential function for different ensembles (clumps and interclump or only interclump medium), different v_{obs}, and for the different transitions analysed in this paper. The worst case relative deviation is about 3%, for most other constellations the deviation is orders of magnitude smaller. As the integral significantly increases the computing time it is only optional in the code and not included in the presented simulations.
As discussed in Sect. 2.3.1 the probability to find a specific combination of clumps (a specific element x_{i} ∈ X_{i}) on a line of sight through the voxel is given by (30)As a next step the effective line intensity and optical depth of each velocity bin is calculated by averaging over all combinations of clumps found in one bin, that is Here, the expected values, μ_{j,i}, and the corresponding standard derivations, σ_{j,i}, depend on mass point and velocity bin. Finally, the contributions from the different bins are summed up to give the complete ensembleaveraged intensity and optical depth In Eqs. (27) and (33), where we sum up the line intensities from different clumps and velocity bins, we have assumed that the line is locally optically thin. By choosing the voxel size Δs sufficiently small, this condition can always be met. The “probabilistic approach” presented in this section for the calculation of the ensembleaveraged quantities was verified by comparison to a second method (see Appendix A). If a voxel contains two ensembles the line intensities and optical depths of both ensembles are calculated separately and summed up, as described in Sect. 2.3.2.
2.3.4. Radiative transfer
In the 3D PDR simulations the geometry of the PDR is replicated using voxels having the volume (Δs)^{3}. To derive maps and spectra that are comparable to observations the radiative transfer through the 3D model needs to be calculated. An example for a 3D setup is shown in Fig. 1 (replicating the Orion Bar PDR, which will be introduced in Sect. 3). Each small cube in Fig. 1 represents one voxel and the colour scale shows the FUV flux at the different voxels within the compound, for an FUV source located at the position [0,22.3,30].
Fig. 1
3D compound replicating a possible geometry (model 1m, see Table 5) of the Orion Bar PDR. Each cube represents one voxel filled with at least one clumpy ensemble. Coordinates are given in voxel sizes, corresponding to 0.01 pc. The colour scale (Green 2011) shows the impinging FUV flux, calculated for each voxel, for a FUV source located at [0,22.3,30]. The direction to Earth corresponds to the positive z direction. 
The attenuation of the FUV photons inside the PDR is given by the sum of the optical depths, ⟨ τ_{FUV} ⟩ _{ens}, of all voxels between the FUV source and the voxel of interest. The KOSMAτ 3D code accounts for the absorption of photons and for isotropic scattering within the individual clumps (see Sect. 2.1). The code uses voxels with the shape of a cuboids, which are oriented in a way that one side of the cuboid is perpendicular to the line of sight between observer and voxel (zaxis). The voxels need to be sufficiently small to trace all relevant changes of different quantities within the compound, but some compromise has to be made to reduce the overall computational effort. Therefore, the KOSMAτ 3D code can make use of a setup where the FUV attenuation is calculated for different positions within the voxels, that is to say on a 3D Cartesian grid at subvoxel scale, and is averaged over the voxel afterwards. For each subvoxel the code derives the line of sight that connects the centrepoint of the subvoxel and the source of the FUV radiation and evaluates which voxels intersect with this line of sight (i.e. shield FUV radiation). The FUV attenuation is weighted by the distance that is effectively crossed by a FUV photon within each voxel. The simulations presented in this work have been performed using a 3 × 3 × 3 voxel subgrid. For the calculation of the ensembleaveraged line intensity and line optical depth (see Sect. 2.3.3), and consequently also for the radiative transfer, the subvoxel grid is not used. This has two reasons: (a) while the lines of sight between the observer and a voxel are oriented perpendicular to the voxel surface, this is in general not the case for the lines of sight between the FUV source and the voxels. If we would calculate the FUV attenuation between FUV source and voxels by just summing up the ⟨ τ_{FUV} ⟩ _{ens} between the position of the FUV source and the midpoints of the voxels we would introduce unnecessarily large errors in the case where a voxel is partly shielded by another voxel in the foreground; (b) the subvoxel treatment is too costly for the calculation of the ensembleaveraged line intensity and optical depth where calculations need to performed at different (here: i_{max} = 21) velocities.
We perform the radiative transfer for the same velocities v_{obs} that have already been used in Sect. 2.3.3. The ensembleaveraged volume emissivity and absorption coefficient, ⟨ ϵ ⟩ _{ens}(v_{obs}) and ⟨ κ ⟩ _{ens}(v_{obs}), are calculated based on the results from the last section, Eqs. (33) and (34), that is where Δs denotes the depth of the voxel along the line of sight. In the following we will omit the velocity dependence (v_{obs}) and the “ensembleaveraged brackets” ⟨ ... ⟩ _{ens} in our formulae for readability reasons.
For radiation travelling a distance ds along a straight path the change in intensity is given by the equation of radiative transfer, which (omitting the dependence on frequency) reads (37)Integration along a straight path length, between 0 and Δs, yields (38)where I_{bg} = I(0) is the background intensity of radiation travelling along the same path. For radiative transfer from voxel p−1 to the neighbouring voxel p (Ossenkopf et al. 2001) ϵ and κ are linearly interpolated. Therefore, we define ϵ = e_{0} + e_{1}s′ and κ = k_{0} + k_{1}s′ with s′ ∈ [0,Δs] and with (39)which can be inserted into Eq. (38) yielding (40)Equation (40) is solved numerically and tabulated for the simulations (see Appendix B).
3. The Orion Bar PDR
The Orion Bar PDR is a prominent feature located in the Orion Nebula (M42, NGC 1976). In observations of typical cooling lines from the UV down to radio wavelength and the IR continuum the bar appears as a bright rim. With a distance of 414 ± 7 pc (Menten et al. 2007) the Orion Bar PDR is one of the nearest and hence brightest PDRs to the terrestrial observer. Consequently, a large amount of observations of the Orion Bar PDR has been performed providing us with an excellent test case for PDR models.
Chemical stratification has been observed for the Orion Bar PDR by different groups (Tielens et al. 1993; van der Werf et al. 1996; Simon et al. 1997; Marconi et al. 1998; Walmsley et al. 2000; van der Wiel et al. 2009; Pellegrini et al. 2009; BernardSalas et al. 2012). For example van der Wiel et al. (2009) discuss a layered structure with C_{2}H emission peaking close to the ionisation front (IF), followed by H_{2}CO and SO, while other species like C^{18}O, HCN and ^{13}CO peak deeper into the cloud.
Nowadays, it has become clear that a “simple” homogeneous (i.e. nonclumpy) bar is an insufficient description of the Orion Bar PDR. High angular resolution observations show that the bar breaks down into substructure. The commonly accepted picture is that the bar includes an extended gas component of n_{H} = 10^{4−5} cm^{3} that causes the chemical stratification and is the dominating origin for lowJ molecular line emission. Embedded in this socalled interclump medium, a clumpy highdensity (n_{H} = 10^{6−7} cm^{3}) component is needed to provide the emission of the highdensity tracers, among others the lines of highJ CO isotopologues, CO^{+}, and the observed H_{2} or OH (for a summary and additional references see Goicoechea et al. 2011). The low filling factor of the dense clumps ensures that the FUV field can penetrate deep into the cloud. We cannot list all observations that have dealt with the spatial structure of the Orion Bar. Just to name a few, Young Owl et al. (2000) presented combined singledish and interferometric data of HCO^{+} and HCN J = 1−0 which show a clumpy NE and SW bar, Lis & Schilke (2003) showed interferometric data of the Orion Bar PDR in H^{13}CN and H^{13}CO^{+} and identify at least ten dense condensations in the H^{13}CN image, and individual clumps have also been resolved by van der Werf et al. (1996) who showed that a PDR surface can be found on each clump inside the Orion Bar. More recent studies on the structure of the Orion Bar PDR have been performed by Goicoechea et al. (2011), Cuadrado et al. (2015).
3.1. Geometry
A common explanation for the existence of the bar is the Blister model: the Orion Nebula embeds a cluster of bright and young stars which ionise their surrounding medium creating an Hiiregion inside the molecular cloud. At the side of the nebula facing Earth this “Hiibubble” has broken out of the cloud, enabling observations of the cavity and of the Orion Bar PDR which forms one of the edges of the cavity, illuminated by the strong FUV radiation from the young star cluster (see for example Wen & O’dell 1995, and references therein).
The dominating ionising source and most massive star is Θ^{1} Ori C which produces ~80% of the Hionising photons. Θ^{1} Ori D, the second most massive star of the Trapezium system, accounts for another ~15% (Draine 2011). The IF, as marked for example by the peak position of the [Oii] or [Feii] emission (Walmsley et al. 2000), [Sii] (Pellegrini et al. 2009), or [Nii] (BernardSalas et al. 2012) is located at 111′′ (corresponding to 0.223 pc) projected distance from Θ^{1} Ori C.
The flux at the IF is estimated to correspond to an enhancement over the average interstellar radiation field, χ_{0}, by a factor ≈4.4 × 10^{4} (Hogerheijde et al. 1995; Jansen et al. 1995) (the series of papers by Hogerheijde et al. 1995; and Jansen et al. 1995, is hereafter abbreviated HJ95). We have verified that this value lies in the probable range (see Appendix C).
Different geometries have been proposed to model the Orion Bar, the dominating idea is a slightly inclined faceon/edgeon/faceon geometry first introduced by HJ95. A schematic picture of this geometry is shown in Fig. 2. Many other workgroups use adoptions of this geometry to model observations (see e.g. Pellegrini et al. 2009). Due to the increased column density along the line of sight, this geometry naturally explains the observed intensity peak. The depth of the cavity, the inclination angle of the bar (α′, not to be confused with the powerlaw exponent α from Eq. (6)) and the zposition (position on the line of sight to the observer) of the illuminating cluster are subject to discussions. Different possibilities are indicated in Fig. 2.
The faceon/edgeon/faceon geometry is consistent with all the farIR and submm observations, but an indication that this geometry needs at least some modifications stems from optical observations (Alves & McCaughrean 2002) that show some shadowing at the very edge of the Orion Bar. This would be explained by a configuration where the Orion Bar is not the edge of a cavity but rather a filament as proposed by Walmsley et al. (2000), Arab et al. (2012).
Fig. 2
Faceon/edgeon/faceon Orion Bar geometry as proposed by HJ95. Values are taken from: green: HJ95; red: Menten et al. (2007); blue: Pellegrini et al. (2009); and orange: van der Werf et al. (2013). For the inclination angle, α′, values between less than 3° and 15° have been discussed (Jansen et al. 1995; Melnick et al. 2012). 
3.2. Observations
A tremendous amount of data is available for the Orion Bar PDR. Recent observations of the Orion Bar PDR, observed with the Herschel Space Observatory (Pilbratt et al. 2010), can for instance be found in Habart et al. (2010), Goicoechea et al. (2011), Nagy et al. (2013, 2015). Recently, the whole Orion molecular cloud 1 region, which includes the Orion Bar PDR, has been mapped velocity resolved by Goicoechea et al. (2015).
As the aim of this paper focuses on the description and the testing of the KOSMAτ 3D code, we selected only observations of abundant and simple species: CO isotopologues, HCO^{+} and the [Cii] cooling line. An expansion including many more species is of course possible.
We used [Cii], CO 10−9, CO 16−15, ^{13}CO 5−4, ^{13}CO 10−9 and HCO^{+}6−5 line observations of the Orion Bar PDR observed with the Heterodyne Instrument for the FarInfrared (HIFI, de Graauw et al. 2010) onboard the Herschel Space Observatory (Pilbratt et al. 2010)^{8}. The observations were performed as part of the EXtraOrdinary Sources (HEXOS) guaranteedtime key program (Bergin et al. 2010). Combined with lowJ CO and HCO^{+} rotational lines (see below) these lines are well suited to trace the chemical stratification observed in the Orion Bar PDR.
The [Cii] observations have already been discussed in Ossenkopf et al. (2013). Furthermore, Nagy et al. (2015) show an HCO^{+} map. All other Herschel data is presented here for the first time. Further analysis of the data will be provided in subsequent papers (Choi et al. 2014; Nagy et al. 2017)^{9}.
All presented HIFI/Herschel observations are strips across the bar with a width of 1′ or more, except for the CO 16−15 observations where a single cut has been observed. The observations were taken in the onthefly (OTF) observing mode around the centre position (α_{J2000} = 5^{h}35^{m}20.81^{s}, δ_{J2000} = −5°25′17.1′′) with a position angle perpendicular to the bar, that is 145° east of north, and an OFF position 6 arcmin southeast of the map. The observations used the WideBandSpectrometer (WBS) with a frequency resolution of 1.1 MHz which corresponds to 0.17 km s^{1} at the rest frequency of the [Cii] line. Both polarizations were averaged to improve the signaltonoise ratio. Integration times varied between 4 and 30 s resulting in noise levels between a 0.02 and 0.3 K. The highfrequency HIFI/Herschel data, in other words the maps of [Cii] and CO 16−15 have been reduced in HIPE as described by Ossenkopf et al. (2013). All other lines were analysed using the GILDAS software^{10} for baseline subtraction and spatial resampling. An overlay of our data, [Cii] overplotting ^{13}CO 10−9, is shown in Fig. 3.
Fig. 3
Orion Bar observed with HIFI/Herschel. The green contours show [Cii] line intensities integrated between 7 and 13 km s^{1}. The contours range between 200 and 800 K km s^{1} in steps of 100 K km s^{1}. The colour scale gives the ^{13}CO 10−9 line intensity, integrated between 9 and 12 km s^{1}. The reference position is the CSO reference position (5^{h}35^{m}20.122^{s}, −5°25′21.96′′). 
The line intensities (Table 2) are given on a T_{mb} scale. For the HIFI/Herschel observations T_{mb} is a factor 1.26 to 1.5 higher than , depending on the respective frequency (Roelfsema et al. 2012). As discussed by Ossenkopf et al. (2013), the scaling from to T_{mb} is questionable for very extended emission (like [Cii]) where the error beam of the telescope is likely to be filled with emission of approximately the same brightness as the main beam. Hence, for extended emission our intensities are upper limits.
Our data set is combined with groundbased observations of CO 2−1, CO 3−2, CO 6−5, ^{13}CO 3−2, ^{13}CO 6−5 and HCO^{+}3−2 observed with the Caltech Submillimeter Observatory (CSO) (Lis, priv. comm.). The CSO observations are typically more extended but overlap with the HIFI/Herschel maps. To facilitate the comparison between the maps, the reference positions of all maps have been shifted to be equal to the CSO reference position (5^{h}35^{m}20.122^{s}, −5°25′21.96′′).
To simplify the analysis of the stratification profile, the maps have been rotated around the CSO reference position by −145° (−145° clockwise), resulting in an orientation of the Orion Bar parallel to the xaxis (see Figs. 3 and 4). As we focus on the stratification of the chemical and excitation structure across the Orion Bar, the observed spectra have been averaged along rows of pixels^{11} parallel to the xaxis ensuring that we average over clumps and interclump medium. In the xrange between −11.3″ and −43.5″ the Orion Bar has a very straight appearance in all of our maps and an average over ~30″ guarantees that we are not affected by individual clumps, but consider a clumpensemble on the observational side as well. Lis & Schilke (2003) observe the size of dense condensations in the Orion Bar and find sizes between 3.81″ and 7.96″ and Young Owl et al. (2000) discuss clumps of 9″ size, supporting our approach.
Gaussian profiles were fitted to the averaged spectra. We have fitted two Gaussian profiles, one profile fixed at a centre velocity of 8 km s^{1} to exclude the emission from the Orion Ridge (van der Tak et al. 2013). The other profile fits the main component at about 11 km s^{1} originating from the Orion Bar. Integration of this component provided the lineintegrated intensity, averaged for the respective row (yposition). The peak position was determined by fitting a parabola to the rowaveraged intensities at the different ypositions. As deviations between the fitting points and the fitted parabolas are very small, we assume the pointing error of the telescope as the main uncertainty in the determination of the peak position. The pointing errors are 2.4″ for HIFI/Herschel (Pilbratt et al. 2010) and 3″ for CSO data^{12}. The resulting peak intensities and yoffsets are summarised in Table 2 for the different transitions. Table 2 indicates a peculiarity of the HCO^{+} 3–2 transition. It seems to peak in front of all the other molecular transitions, including HCO^{+} 6–5 that should tracer warmer gas, while the profiles of both lines are very similar. We have no evidence for a pointing problem in these data so that we stick to the formal errors, but as there is no physical scenario that would explain this peak offset we rather question the role of the HCO^{+} 3–2 peak position in the fit of the stratification pattern in the discussion (Sect. 5.4.1).
Fig. 4
[Cii] integrated intensity (same as the contours in Fig. 3, but rotated by −145°). In this orientation Θ^{1} Ori C in the northwest of the bar is at the bottom of the map. The green line marks the cut with the highest averaged lineintegrated intensity, including all pixels with xoffsets between −11.3′ and −43.5′. 
Summary of averaged integrated intensities and spatial offsets of the observations.
4. 3D Model of the Orion Bar PDR
We have composed a 3D model of the Orion Bar PDR from cubic voxels with an edge length of 0.01 pc, corresponding to 5.0″ at the distance of 414 pc. The voxel size is small enough to trace physical changes in the PDR and to analyse stratification effects, but large enough to ensure that the total number of voxels can be treated on a standard PC. Furthermore, for all observations that have been fitted in this work, the resulting pixel size is at least a factor two smaller than the beamsize. Our Cartesian coordinate system was chosen in such a way that the xdirection is parallel to the Orion Bar and the zdirection points towards the observer. As we were mainly interested in the stratification of the Orion Bar here, the current model ignores any variation of the density structure in xdirection. This reduces the number of free parameters, but excludes for the moment the simulation of additional structures like the Orion Ridge.
In this work we focus on geometries for the Orion Bar PDR that are based on the HJ95 series of papers, in other words on geometries that consist of an almost edgeon cavity wall facing the illumination from Θ^{1} Ori C (see Figs. 1 and 2). Aiming for a fit of the observations presented in Sect. 3.2 different parameters have been varied in this model setup. An overview over these parameters is provided in Sect. 5.1. In Sect. 5.2 we discuss the measures that are used to evaluate our fits. A second geometry that has been discussed for the Orion Bar is the filament model proposed by Walmsley et al. (2000) and Arab et al. (2012). This model consists of a cylinder in the plane of the sky with the main symmetry axis along the bar (see Fig. D.2). In Appendix D we show preliminary tests of this geometry, which indicate that a simultaneous reproduction of the observed stratification pattern and the line intensities based on the cylindrical model is problematic. For this geometry, the short lines of sight through the compound close to y = 0 enforce that the emission peaks appear deep in the cloud, where the FUV flux is low. This reduces the lineintegrated intensities and increases the scatter between the yoffsets calculated for the different transitions.
The main illuminating source Θ^{1} Ori C is 111″ away from the IF. This corresponds to a separation by 22.3 voxels in ydirection between star and interface. In xdirection, the location of the star defines our zero point, meaning that in voxel units Θ^{1} Ori C is located at [0,y_{IF} + 22.3,z_{star}] in the model. The z position of the star (z_{star}) is not exactly known (see Fig. 2) and has become one of our fitting parameters.
Based on C^{18}O 3−2 observations and assuming a conversion factor of N_{H2}/N(C^{18}O) = 5 × 10^{6}, HJ95 derive a total H_{2} column density of N_{H2} = 6.5 × 10^{22} cm^{2} along the line of sight (peak) for a path length of 0.6 pc. For a uniform density along the line of sight this translates into n_{H2} = N_{H2}/ (0.6 pc) = 3.5 × 10^{4} cm^{3}. Consequently, the total average mass in a voxel with volume (0.01 pc)^{3} is: (41)which we used as baseline for our simulations.
The clump ensembles in the models contain clumps at the mass points [10^{3},10^{2},10^{1},10^{0}]M_{⊙}, implying that one voxel typically only contains fractions of clumps, meaning that N_{j}< 1. The upper mass limit matches the resolved clump masses in the range 0.5−1.5M_{⊙} determined for the Orion Bar PDR by Lis & Schilke (2003). The lower limit of 10^{3}M_{⊙} is used as the smallest mass contained in the available KOSMAτ input grid because, to gain a good approximation of a fractal geometry, the inclusion of very small structures is desired. We discuss this choice and show simulation results based on models using different mass points in Sect. 5.3.5. In the KOSMAτ 3D code the pixels are scaled to superpixels (see Sect. 2.3.1). In the simulations presented here, after the scaling process, each “supervoxel” usually contains one clump at mass point 10^{0}M_{⊙} and consequently { N_{j} } = [331,48,7,1] for the different mass points (see Eq. (19), results rounded to integer values).
The thin interclump medium is mimicked by a second clump ensemble with an averaged density that is about two orders of magnitude lower than the averaged density of the dense clumps. To approximate a relatively homogeneous interclump medium, we started our simulations using small clumps of 10^{2}M_{⊙}. Furthermore, the VFF of the interclump medium should be equal to unity or smaller. Therefore, we needed to add the condition (42)or equivalently (43)For a discussion of the interclump parameter M_{inter, tot} and ρ_{inter} see Sect. 5.1. We started our simulations with a VFF of unity in Sect. 5.3.1. Different choices for the mass point and the VFF of the interclump medium are tested in Sect. 5.3.6.
In this work we did not fit full line profiles. Therefore, the velocity spread between the clumps in one ensemble (σ_{j, ens}, see Eq. (26)) has been fixed. We discuss our choice of the σ_{j, ens} and show examples of simulated line profiles in Sect. 5.3.7.
The KOSMAτ 3D code allows for the simulation of (2D) maps. As an example, Figs. 5 and 6 show simulated maps of lineintegrated CO 3−2 intensities, before and after the convolution with a Gaussian beam of 21.9″ FWHM, matching the CSO beam used in the observations. The maps are based on model 1m (see Table 5). We find a combination of the imprint of the sharp edge of the bar and a curvature stemming from the varying distance to the illuminating star. The convolution blurs the edge and the emission peak, but still allows to recover the stratification of the emission.
For our systematic parameter study we have reduced the map size (and the beam convolution) to a cut of only one pixel in xdirection across the Orion Bar. Such a cut enables us to derive the lineintegrated intensities and peak offsets within a computing time of about six hours^{13}. For the 2D maps 18 days of computing time are needed. Typical simulated cuts are shown in Fig. 7 based on model 6j (see Sect. 5.3.6 and Table 5).
Fig. 5
Simulated map of lineintegrated CO 3−2 emission of the Orion Bar PDR, based on model 1m (see Table 5). The coordinates are given in units of pixels. One pixel corresponds to 0.01 pc or 5″ on the sky. The illuminating star Θ^{1} Ori C is located at x = 0 and y = 22.3 on top of the map. 
The maps of the Orion Bar can include radiation from the background molecular cloud (see Fig. 2). Therefore, we should in principle extend our model into the negative zdirection until we have reached a depth were none of the investigated tracers is excited anymore. However, to reduce computing time, the background molecular cloud was cut off at z = −20 in our systematic parameter study. At z = −20 the FUV flux has usually dropped below one Draine field (see Fig. 1). To investigate possible contributions to the final maps/cuts from the background molecular cloud we have rerun the simulation of model 2b (which provides one of the best fits of the lineintegrated intensities; see Sect. 5.3.3 and Table 5), but with the compound extended to z = −100.
Figures 8 and 9 show simulated cuts through the Orion Bar model 2b, before the extension. The colour scales in these plots give the line centre intensity emitted by each voxel, in Fig. 8 for CO 2−1 and in Fig. 9 for CO 16−15. The figures show that CO 16−15 is only excited close to the PDR surface, where the FUV flux is relatively high. Hence, the background molecular cloud will not be visible in the final line maps. For CO 2−1 the situation is different: the excitation only depends weakly on the FUV flux and hence, the voxels still emit at z = −20. However, the effect of adding the background cloud to the simulation is still small due to the high optical depth of the CO 2−1 line. Overall, we find that adding the background molecular cloud slightly changes the quality of the fit of the lineintegrated intensities (see Table 5), but it does not change the outcome of our systematic parameter study.
Fig. 6
Same as Fig. 5 but after convolution with a Gaussian beam of 21.9″ or 4.4 pixels FWHM (see Table 2). 
Fig. 7
Simulated cuts perpendicular to the Orion Bar, based on model 6j (see Table 5). Each colour scale gives the lineintegrated intensity of the transition indicated above the respective cut. 
The total column density of the Orion Bar can be constrained from optically thin tracers that are only weakly sensitive to the PDR conditions. HJ95 provide lineintegrated intensities of the C^{18}O 2−1 and 3−2 transitions at the emission peak of the Orion Bar PDR. Due to the low optical depths of these transitions compared to the other CO isotopologues, they provide an upper limit for the total (volumeaveraged) column density of the dense clumps, including the background cloud. Table 3 compares the intensities from HJ95 to the simulated lineintegrated intensities based on models 2b, 6j, and 2b_ext, having cutoffs at z = −20 and at z = −100. In contrast to all PDR simulations, HJ95 observed a C^{18}O 2−1 line that is significantly weaker than the 3−2 line. This could be explained by a cold foreground layer. However, as we have not included foreground material into our models, a detailed fit of that line is beyond the scope of this work. Therefore, we concentrate on the C^{18}O 3−2 line for the column density estimate like HJ95.
We find that the contribution from the interclump medium to the C^{18}O 2−1 and 3−2 line emission is negligible. Furthermore, the increase of the C^{18}O lineintegrated intensities due to the background extension is low. Using the HJ95 column density of N_{H2} = 6.5 × 10^{22} cm^{2} leads to line intensities that are too low by more than a factor of two. Models 2b and 6j contain a mass per voxel of 2 M_{HJ} combined with a total depth of 0.8 pc (cutoff at z = −20, parameters M_{cl, tot}, d_{cavity}, see Sect. 5.1). The total (volumeaveraged) column of the ensembles of dense clumps is N_{H2} ≈ 1.7 × 10^{23} cm^{2}, a factor 2.7 higher than the value that was found by HJ95. The two models provide intensities that are too high by 40% and 25% compared to the observations so that we consider the column density of 1.7 × 10^{23} cm^{2} as the upper limit. Consequently, we have excluded models with higher column densities from our simulation runs. Lower columns are always allowed in our models, as they could be compensated by a deeper background cloud that is invisible in all the PDR tracers discussed here.
Fig. 8
Cut through the Orion Bar model 2b. For each voxel the colour scale gives the CO 2−1 line intensity of dense clumps and interclump medium, at the line centre (at 11.3 km s^{1}). The illuminating star Θ^{1} Ori C is located at [0,22.3,30]. 
5. Parameter scans
5.1. Parameters
In the following we summarise the parameters that are varied within our simulation runs. If available we also give values taken from HJ95 which will be used as an initial guess for our simulations.
M_{cl, tot}: the mass contained in dense clumps per voxel. Based on HJ95 we have estimated the total mass per voxel, M_{HJ}, in Eq. (41). Furthermore, HJ95 state that about 10% (i.e. 0.1 M_{HJ}) of the molecular material^{14} is contained in clumps.
M_{inter, tot}: the mass contained in the interclump medium per voxel. Following HJ95 the interclump medium accounts for 90% of the total molecular column density. Using their values, that is ρ_{inter} = 2 n_{H2} = 2 × 3 × 10^{4} cm^{3} and a total interclump mass of 0.9 M_{HJ} in one voxel with a volume of (0.01 pc)^{3}, the VFF is about 1.05 (see Sect. 4; per voxel this corresponds (statistically) to 0.156 clumps with a mass of 10^{2}M_{⊙} and a volume of 6.74 × 10^{6} pc^{3}). In some of the presented simulations (see Sect. 5.3.1) M_{inter, tot} is no independent parameter, but coupled to ρ_{inter} to ensure an interclumpVFF of unity. Therefore, in our first simulation runs where M_{inter, tot} (and ρ_{inter}) were varied, ρ_{inter} = 6 × 10^{4} cm^{3} corresponds to M_{inter, tot} = 0.848M_{HJ} (instead of 0.9 M_{HJ}).
ρ_{cl}: the ensembleaveraged hydrogen nucleus density of the dense clumps. HJ95 derive cm^{3} (i.e. ρ_{cl} ≈ 2 × 10^{6} cm^{3}) using only one type of dense clumps.
ρ_{inter}: the ensembleaveraged hydrogen nucleus density of the interclump medium. HJ95 derive cm^{3} (i.e. ρ_{inter} ≈ 2 × 3 × 10^{4} cm^{3}) for a homogeneous interclump medium. In some simulations ρ_{inter} is coupled to M_{inter, tot} (see above).
α′: the inclination angle of the bar (see Fig. 2). Jansen et al. (1995) discuss that an inclination angle α′< 3° is probable.
z_{star}: the zposition of the illuminating source, which is not discussed in HJ95. Here, we started our simulations using z_{star} = 0.3 pc, on other words with the illuminating source located at half height of the cavity.
I_{UV}: the FUV flux from the illuminating source, Θ^{1} Ori C, at the position of the IF. HJ95 state 4.4 × 10^{4}χ_{0}. We provide an estimate of I_{UV} in Appendix C. The flux I(r) at a position r of a voxel at the cloud surface (i.e. not affected by FUV absorptions) is given by (44)where 0.223 pc is the observed distance in ydirection between Θ^{1} Ori C and the Orion Bar (see Sect. 3.1), r_{star} is the position of the illuminating source in our model and Δs is the edgelength of one voxel in pc.
d_{cavity}: the depth of the cavity (see Fig. 2). HJ95 assume 0.6 pc.
d_{clumps}: the depth into the cloud at which dense clumps appear. With this parameter we have tested whether the same amount of clump and interclump mass is contained in each voxel, independent from the depth into the PDR, or if there is there a process that removes dense clumps from the surface. The models discussed in HJ95 do not account for such an effect, but it is proposed in their text.
m_{l,cl}: the lowest mass point of the ensemble of dense clumps. HJ95 find that a singledensity model cannot explain the observed line ratios and assume that a range of densities appears in the beam. They construct their model with two density components (clump and interclump medium) as a first order approximation. Here, we started our simulations with dense clumps down to 10^{3}M_{⊙}.
m_{inter}: mass point used for the interclump medium. The interclump medium is represented by identical clumps of mass m_{inter}. Here, we used m_{inter} = 10^{2}M_{⊙} as initial guess.
Overview over fitting parameters.
5.2. Model assessment
To evaluate the goodness of the fit, accounting for the simultaneous reproduction of the integrated line intensities and the Orion Bar stratification structure, we have determined the yposition and the integrated intensity of the pixel with the highest integrated intensity for each transition from our simulated cuts.
The stratification is measured in terms of the yoffset of the intensity peak relative to the [Cii] peak position: Δy_{i} = y_{[CII]}−y_{i} where the index i refers to different transitions (a negative Δy_{i} indicates a shift towards Θ^{1} Ori C). Simulations and observations have been compared by deriving the difference between the respective offsets, (45)where Δy_{obs,i} refers to the observations (by definition, y_{diff,CII} = 0 for [Cii], our reference coordinate). The relative differences between simulated and observed peak integrated intensities are given by I_{rel,i} = I_{fit,i}/I_{obs,i}. We summarise the y_{diff,i} and the I_{rel,i} of different models and transitions in scatter plots (see for example Figs. 10 and 11), which yield a clear way to compare the models and to identify systematic behaviour. In addition, we define measures to evaluate the goodness of our fits. For the yoffsets we have used a chisquare test, namely (46)where Err(Δy_{obs,i}) denotes the error of the offsets derived from the observations, as stated in Table 2, and the sum runs over all simulated transitions. A typical chisquare test, as used in Eq. (46), evaluates the model in terms of absolute (squared) differences between the observed and the fitted values. For the lineintegrated intensities we aimed for a figure of merit which evaluates our models in terms of factors, for instance a simulation result that deviates from the observations by a factor two has the same quality as a results that is wrong by a factor of one half. Therefore, we define (47)where the errors Err(I_{obs,i}) are stated in Table 2 and the denominator has been derived by error propagation, that is (48)When parameters are varied during the fitting process the line intensities and the yoffsets are usually affected in a very different manner. To make the effects of parameter variations visible we state and separately for each simulation run. However, to evaluate how well the stratification pattern and the lineintegrated intensities are matched by a specific model we use the sum (49)Furthermore, the quality of a fit in the statistical sense is given by the reduced chisquare (50)where f = N−M are the degrees of freedom (see Press et al. 1992). Here, N = 23 is the number of quantities that are fitted, namely the lineintegrated intensities of 12 different transitions plus the 11 yoffsets relative the [Cii] peak position, and M is the number of parameters that can be adjusted. In this work M varies between different series of simulations.
The best method to derive a good fit would be to systematically explore the parameter space in all directions. Unfortunately, due to the large number of free parameters, this cannot be done within an acceptable amount of computing time. Hence, we have chosen the following approach: the values taken from HJ95, as summarised in Sect. 5.1, were used as an initial guess for our simulations. Successively “series of models” have been run, where in each series at least two parameters have been varied at a time. Based on the test the best model from each series was then selected and the parameters have been kept for the next series. In case of interdependences between parameters we tried to vary these parameters at the same time.
5.3. Simulation runs
Overview of different model setups.
5.3.1. Ensembleaveraged densities and masses per voxel
Fig. 10
Scatter plot of the lineintegrated intensities for selected models from Sects. 5.3.1, 5.3.3 and 5.3.4. For each transition the ratio between simulated and observed lineintegrated intensity at the respective peak position, I_{rel,i} = I_{fit,i}/I_{obs,i}, is plotted on a logarithmic scale. The different transitions are indicated on the abscissa and different symbols mark the different models. 
Fig. 11
Scatter plot of the yoffsets of selected models from Sects. 5.3.1, 5.3.3 and 5.3.4. For each transition the difference between the offset of the simulated and the observed peak position, as defined in Eq. (45), is plotted. All offsets are relative to the [Cii] peak position, hence, for the reference [Cii] transition y_{diff} is always zero. A negative offset indicates that the simulated emission peak is shifted too far into the direction of Θ^{1} Ori C, a positive offset indicates that the emission appears too deep in the cloud. The different transitions are indicated on the abscissa and different symbols mark different models. 
Each voxel within a compound is filled by two ensembles, one representing the dense clumps and one representing the interclump medium. We started our simulation runs investigating models where these ensembles are the same within each voxel (i.e. d_{clumps} = 0, see Sect. 5.1) and refer to these models as homogeneous. Examples of inhomogeneous models are given in Sect. 5.3.6.
The total mass of the clump and interclump medium contained in each voxel, as well as the related ensembleaveraged densities, have a strong influence on the simulation outcome. Especially in the homogeneous models, these parameters are interdependent: both components can contribute to the FUV attenuation and hence control the line intensities emitted by both components. (In model 1c the dense clumps do only account for 1.4% of the total FUV attenuation. In model 1m, where the total mass of the ensemble of dense clumps has been increased, 29% of the total FUV attenuation is due to this ensemble.) Therefore, we varied these parameters (M_{cl,tot}, ρ_{cl}, M_{inter,tot} and ρ_{inter}) first. For all other parameters we have used our initial guess (Table 4). An overview of different model setups is given in Table 5.
In our first series of models (represented by models 1c to 1D in Table 5) we have coupled ρ_{inter} to M_{inter,tot}, enforcing a VFF of unity for the interclump medium. In these runs we have tested ensembleaveraged densities of 10^{6}, 2 × 10^{6} and 4 × 10^{6} cm^{3} for the dense clumps combined with total ensemble masses between 0.1 and 2 M_{HJ}. An ensembleaveraged density of 4 × 10^{6} cm^{3} combined with the four mass points implies that the smallest clumps have densities of about 10^{7} cm^{3} (see Eq. (18)). Higher densities are not possible with the current input grid (see Table 1). As discussed in Sect. 4 models with M_{cl,tot}> 2M_{HJ} (combined with d_{cavity} = 0.6 pc) have been excluded. For the interclump medium we have tested densities between 6 × 10^{3} and 10^{5} cm^{3}, which implies total masses per voxel between 0.0858 and 1.43 M_{HJ}. For these models the degree of freedom (see Sect. 5.2) is f = 23−3 = 20 if a VFF of unity is enforced and f = 19 if M_{inter,tot} and ρ_{inter} are treated independently.
In models 1c and 1m or similarly in models 1d, 1i and 1n, the total mass of the dense clumps has been increased from 0.1 to 2 M_{HJ} while all other parameters remain unchanged. From all tested models the parameters of model 1d are closest to the values from HJ95. Figure 10 gives an overview over the ratios between simulated and observed lineintegrated intensities for selected transitions. We find that the “HJ95model” 1d does not reproduce any of the fitted lineintegrated intensities, except for [Cii]. However, all other intensities are too low, often by orders of magnitude. Model 1i uses the same parameters, but with M_{cl,tot} increased to 0.5 M_{HJ}, which increases the line intensities of the species that are (dominantly) emitted by the dense clumps, namely the highJ CO isotopologues and the HCO^{+} transitions. Still, the resulting line intensities are too low, except for [Cii]. In model 1n, which uses M_{cl,tot} = 2M_{HJ}, the line intensities of most transitions are still too low, but the fit does significantly improve compared to the models discussed above.
Model 1j uses the same parameters as model 1i except for the density (and therefore also the total mass) of the interclump medium, which has been increased. In Fig. 10 one can see how the lineintegrated intensities of the transitions which are (at least partially) emitted by the interclump medium, namely [Cii] and the lowJ CO isotopologues, increase. The line intensities of the other transitions decrease due to the stronger FUV attenuation in the cloud.
Overall, we find that increasing M_{cl,tot} improves the quality of our fit (lower ). Furthermore, from comparing for example model 1m with 1B (or 1n and 1C; see Table 5 or Fig. 10) one can see that a higher ensembleaveraged density of ρ_{cl} = 4 × 10^{6} cm^{3} provides lower and, although can increase, lower .
The spatial offsets of the different peak positions do mainly depend on the FUV attenuation in the cloud and on the peak position of the [Cii] line which is the reference for all other transitions. Figure 11 gives an overview over the y_{diff,i} (see Eq. (45)) of the models that have already been included in Fig. 10. For models 1d and 1i we find that the emission peaks of the CO 2−1 and ^{12/13}CO 3−2 transitions (for model 1d also of the ^{13}CO 5−4 and ^{12/13}CO 6−5 transitions) are shifted too far into the cloud by about five to nine pixels (0.05 to 0.09 pc). For model 1c (not shown separately but with a high in the table) the CO 2−1 and the ^{12/13}CO 3−2 emission peaks are shifted even further into the cloud, they appear about 20 pixel behind the [Cii] emission peak. Based on these transitions and for the current setup, we have to conclude that the FUV attenuation in the cloud is significantly too weak. However, for most other transitions, the offsets are found to be too small and hence a deeper FUV penetration would be necessary to increase their yoffsets. Model 1j with M_{cl,tot} = 0.5 and M_{inter,tot} = 1.43 shows a similar, but less pronounced behaviour compared to models 1d and 1i (see Fig. 11). We conclude that a fit of the stratification pattern based on the setups presented in this section and an interclumpVFF = 1 is not possible.
5.3.2. Reduction of the interclump medium
For models 1E to 1aa the constraint that the interclumpVFF needs to be unity has been dropped. Models 1z to 1aa in Table 5 use M_{cl,tot} = 2 and ρ_{cl} = 4 × 10^{6} cm^{3}. Models 1z to 1D show how the composition of the interclump medium affects the fit in terms of the chisquare tests. We find that the fit improves in terms of and if the amount and VFF of the interclump medium is reduced, allowing for a deeper FUV penetration into the cloud. The are somewhat more random, which is probably due to changing yoffsets of the [Cii] reference position (see below). The models with a interclump−VFF ≪ 1, in other words models 1E, 1bb and 1P, provide similar values and we cannot discriminate between these models based on our setup. However, as we needed to decide with which model we wanted to continue our simulations in the next section, we computed with a higher precision than integer values in Table 5. Based on these results, model 1P with provides the best fit of all models discussed so far. It uses ρ_{inter} = 10^{5} cm^{3} and M_{inter,tot} = 0.1M_{HJ}, resulting in an VFF of 0.07 for the interclump medium. We note that for our best fitting models the intensities of the CO 16−15 and HCO^{+}6−5 transitions are somewhat too high here compared to the observations (see model 1P in Fig. 10).
In all models the simulated yoffsets of the line peak positions of (nearly) all transitions tend to be too low. The best fit in terms of the stratification pattern () is obtained by model 1E (see Table 5). However, for this model the simulated yoffsets relative to the [Cii] emission peak are too small for all simulated tracers (except HCO^{+}3−2, see Fig. 11) and some transitions (^{12/13}CO 6−5 and CO 16−15) appear at the same offset as [Cii]. Furthermore, in model 1E the interclump medium has been completely removed, while the existence of some interclump medium has been deduced from several observations. Overall, model 1P provides the best combined fit of intensity and stratification, but when we actually look at the stratification pattern it becomes clear that the fit of the pattern is not satisfactory. Good fits of the stratification pattern are only found if inhomogeneous models are used (see Sect. 5.3.6).
5.3.3. α′ and I_{UV}
In a second series of simulations the effects of varying the inclination angle α′ and the FUV flux at the cloud surface have been investigated. We have tested inclination angles of α′ = 0°, 3°, 7° and 15°, and have simultaneously increased the FUV flux by 25% and by 50% compared to the original HJ95 value. All other parameters in this second series have been adopted from the best model from the previous section, namely model 1P. As we optimised two additional parameters in this section we have used f = 23−6 = 17 degrees of freedom for the calculation of the .
For all tested α′ and in the tested parameter range, the fit of the lineintegrated intensities depends only weakly on I_{UV} (see for example the comparison between models 1P and 2b, where the FUV flux at the PDR surface has been increased by 50%, in Table 5). Based on the , the best fit of the lineintegrated intensities is provided by models 2b, 2e and 2h (, 273 and 272 respectively), which use I_{UV} = 6.6 × 10^{4}χ_{0} combined with α′ = 3°, 0° or 7°. These models are found to fit the lineintegrated intensities of all transition within a factor of about two or better, for model 2h we find I_{rel,CO 6−5} = 0.49 and I_{rel,HCO+ 6−5} = 2.04 with the I_{rel} of all other transitions lying between these values. For models 2b and 2e the I_{rel} are slightly higher (see also model 2e in Fig. 10). As increasing I_{UV} mainly increases the line intensities of the “outlier transitions” (see Sect. 5.4.2), we have not tested models with I_{UV} higher than 6.6 × 10^{4}χ_{0}.
Changing the inclination angle has two effects. In general, choosing a small α′ provides more excited column along one line of sight through the compound and hence increases the lineintegrated intensities of the optically thin transitions. However, increasing α′ broadens the emission peak, which can also lead to an increase of lineintegrated intensities after the beam convolution. Overall, we find that changing α′ from 3° to 0° or 7° has an negligible effect on the lineintegrated intensities, however, for α′ = 15° the fit becomes worse (see for example model 2i in Table 5).
A more significant effect is that the fit of the stratification pattern is found to be best for α′ = 0°, independent from the choice of I_{UV}. For larger inclination angles, especially for α′ = 15°, increased. The lowest is provided by model 2e, which fits the yoffsets of two transitions (CO 2−1 and HCO^{+}3−2) within the observational uncertainty, however, for all other transitions the simulated yoffsets relative to the [Cii] peak are too small. Model 2e is also included in the scatter plots, Figs. 10 and 11.
5.3.4. d_{cavity} and z_{star}
In a third simulation run two geometrical parameters have been varied, the depth of the cavity that defines the length of the line of sight through the bar (d_{cavity}) and the zposition of the illuminating source (z_{star}). These parameters are partly interdependent: the column of material that is excited in the bar depends on the position of the star relative to the cavity wall. We have varied both parameters, d_{cavity} and z_{star}, between 0.1 and 0.6 pc, which includes the range of values found in literature (see Fig. 2). In addition, we have investigated two models where the star is lying outside of the cavity (d_{cavity} = 0.3 pc and z_{star} = 0.4 pc, and d_{cavity} = 0.6 pc and z_{star} = 0.7 pc). In Sect. 4 we have derived an upper limit for the total molecular column density along a line of sight through a model compound, which was found to correspond to M_{cl,tot} = 2 M_{HJ} for d_{cavity} = 0.6 pc. Hence, for the models where d_{cavity} has been reduced, we also tested models where M_{cl,tot} has been increased to provide the same upper column density limit (see for example models 3r and 3n in Table 5). All other parameters used in this section have been adopted from model 2e. For the degree of freedom we used f = 23−8 = 15 as eight different parameters have been adjusted at this point.
Briefly, the result of this simulation run is that model 2e, which has the deep cavity (d_{cavity} = 0.6 pc) with the star at halfheight (z_{star} = 0.3 pc) is already the best configuration. Model 3a (see Table 5) with the very shallow cavity (d_{cavity} = 0.1 and z_{star} = 0.1 pc) can be excluded, because in this model the emission of some transitions from the back of the cavity (“below” the illuminating source) is about as strong as the emission from the bar itself. Most extremely for HCO^{+}6−5 the emission at y ≳ 20 of the simulated cut is significantly stronger than the emission from the bar, causing the high of this model.
For all models where the total column density of the ensembles of dense clumps is reduced compared to model 2e, the simulated line intensities decrease for all transitions, increasing the and hence decreasing the quality of the fit (see for example model 3c). On the other side, for models 3n (d_{cavity} = 0.3 pc, z_{star} = 0.3 pc and M_{cl,tot} = 4 M_{HJ}) and 3r (d_{cavity} = 0.4 pc, z_{star} = 0.4 pc and M_{cl,tot} = 3 M_{HJ}), which have the same column density of the dense clumps as model 2e, the fit of the lineintegrated intensities improves slightly compared to model 2e. This improvement stems from two effects. Placing the illuminating source close to the outer edge of the cavity puts the hottest material in the PDR closest to the observer. Therefore, after the radiative transfer, the lineintegrated intensities tend to be increased. Second, if the same amount of material is comprised in a shorter bar, the material is – on average – closer to the illuminating source, which increases the lineintegrated intensities (see Sect. 5.3.3). Therefore, the of a model with the same column density as models 3n and 3r, but with d_{cavity} = 0.6 pc and z_{star} = 0.6, is slightly higher. However, as model 2e provides a better fit of the stratification pattern, is still has the lowest and .
5.3.5. m_{l,cl} and m_{inter}
Fig. 12
Same as Fig. 10, plotted for selected models from Sects. 5.3.5 and 5.3.6. In addition, model 2e from Sect. 5.3.3 is given for comparison. 
Fig. 13
Same as Fig. 11, plotted for selected models from Sects. 5.3.5 and 5.3.6. In addition, model 2e from Sect. 5.3.3 is given for comparison. 
As discussed in Sect. 4, all models presented so far have used an ensemble with four mass points, [10^{3},10^{2},10^{1},10^{0}]M_{⊙} (i.e. m_{l,cl} = 10^{3}M_{⊙}), for the ensemble of dense clumps. Only the upper limit of one M_{⊙} can be inferred from observations. Furthermore, the interclump medium was represented by identical clumps of 10^{2}M_{⊙}. In this section, we show the impact of these choices. For the lower cutoff mass of the ensemble of dense clumps we used m_{l,cl} = 10^{2}M_{⊙} and m_{l,cl} = 10^{0}M_{⊙} in addition to the initial value, where the second choice implies that the ensemble only contains clumps with one M_{⊙}. Each of these choices was combined with m_{inter} = 10^{3}, 10^{2}, 10^{1} or 10^{0}M_{⊙} for the clumps representing the interclump medium. For the degrees of freedom we have used f = 23−10 = 13 as ten different parameters have been adjusted so far.
Independent of the choice of m_{inter}, we find that the models that use m_{l,cl} = 10^{3}M_{⊙} provide the best fits. For example models 2e, 4b and 4d (see Table 5) all use m_{inter} = 10^{2}M_{⊙}, but different m_{l,cl}. The I_{rel} of the different transitions of these models are shown in the scatter plot Fig. 12. We can see how the line – intensities of the different transitions systematically decrease when m_{l,cl} increases, especially CO 16−15 is affected. As the lineintegrated intensities of all transitions except for CO 16−15 and HCO^{+}6−5 tend to be too low, increases with increasing m_{l,cl}.
Varying the mass of the individual clumps of the interclump medium (while keeping the total mass M_{inter,tot} fixed) changes the FUV attenuation and hence has an impact on the lineintegrated intensities and on the stratification pattern. For example for ρ_{inter} = 10^{5} cm^{3} and M_{inter} = 0.1M_{HJ} used in this series, decreasing m_{inter} from 10^{2} to 10^{3}M_{⊙} increases the FUV attenuation per voxel by a factor 1.7, due to the more even distribution of the ISM. The line emission of the interclump medium also depends on the size of the individual clumps. Model 4f shows that an increase of the clump size in the interclump medium reduces the emission from the interclump gas, shifting the peak of the [Cii] line into the molecular cloud so that the observable stratification is strongly reduced. The optimal choice of m_{inter} depends on m_{l,cl}, however, for the models that use m_{l,cl} = 10^{3}M_{⊙} and hence provide the best fits, the further result clearly favour m_{inter} = 10^{2}M_{⊙}. Overall, we find that model 2e, with m_{l,cl} = 10^{3}M_{⊙} and m_{inter} = 10^{2}M_{⊙} provides the best fit in terms of stratification and lineintensities.
5.3.6. Inhomogeneous models
Based on the homogeneous models shown so far, the lineintegrated intensities can be fitted within a factor of about two for all simulated transitions. However, the homogeneous models fail in reproducing the observed stratification pattern (see discussion of models 2h/2e in Sect. 5.3.3). Furthermore, different authors (Parmar et al. 1991; HJ95; Young Owl et al. 2000) propose that the ISM in the Orion Bar is not uniformly distributed. Instead they present observations and models, which suggest that the inset of dense material, embedded in the thinner interclump medium, is only found at about 10″−20″ into the cloud.
Consequently, we have tested series of models incorporating such a step. We have limited ourselves to the idealised density profile that is produced if the parameter d_{clumps} (see Sect. 5.1) is chosen to be larger than zero. For these inhomogeneous models the FUV attenuation close to the PDR surface (as defined by d_{clumps}) only depends on the composition of the interclump medium. The dense clumps only contribute deep in the cloud, hence the impact of varying ρ_{cl} and M_{cl,tot} becomes smaller.
We have varied the parameter d_{clumps} to be equal to 0.02, 0.03 and 0.04 pc, as suggested by the observations. As the FUV attenuation within the cloud does significantly change compared to the homogeneous models when the dense clumps are removed, we have simultaneously varied the composition of the interclump medium a second time. Therefore, we have tried values of ρ_{inter} between 6 × 10^{3} and 10^{5} cm^{3} and M_{inter} between 0.05 and 0.5 M_{HJ}. Furthermore, previous simulation runs have shown that for some inhomogeneous models α′ = 3° provides better results than α′ = 0° and hence we have repeated the simulation runs of our most promising models with α′ = 3° instead of α′ = 0°. In Table 5 the names of the inhomogeneous α′ = 0°models start with a 5 (only model 5h is listed), while the names of the inhomogeneous α′ = 3°models start with a 6. All other parameters have been adopted from model 2e. As, compared to the models from the previous section, we have fitted one additional parameter (d_{clumps}), we have used f = 23−11 = 12 for the calculation of the related .
The bestfitting models of these simulation runs are (sorted for increasing and decreasing in Table 5) models 6b, 5h, 6a, 6j, 6k and 6l. We find that all of these models use M_{inter} between 0.1 and 0.4 M_{HJ} combined with ρ_{inter} = 10^{5} cm^{3} and hence have VFFs of the interclump medium between 0.07 and 0.28. For these models and based on our setup we cannot clearly discriminate between α′ = 0° and α′ = 3°. Models 6a, 6j, 6k and 6l provide lower values than the corresponding α′ = 0°models (not shown) while model 5h is slightly better than model 6b. Furthermore, d_{clumps} is 0.02 or 0.04 pc for these models. The observed stratification pattern is not sensitive to the exact choice.
Models 6b, 5h and 6a provide the best fit of the lineintegrated intensities ( or 273) of all models that have been investigated in this section, but they do not provide any improvements compared to model 2e from Sect. 5.3.3. In terms of the fit of the line intensities, the inhomogeneous model does not provide any improvement, but it also does not deteriorate the fit. The situation is very different when considering the stratification pattern. Model 6l provides as low as 15, which has not been reached by any other model. In Fig. 13 we can see how for model 6l the relative yoffsets lie in the (0 ± 0.01) pc interval for all transitions, except for the HCO^{+}3−2 transition. The I_{rel} simulated for model 6l lie between 0.33 (^{13}CO 10−9) and 1.04 (CO 16−15; see also Fig. 12).
Models 6j and 6k (see Table 5 and Figs. 12, 13) are examples for compromises between models 2e/6b/5h/6a and model 6l. Model 6k fits the yoffsets of all transitions within 0.016 pc and has I_{rel} between 0.4 and 1.3. For model 6j the yoffset of ^{13}CO 10−9 is too small (−0.026 pc), for all other transitions the yoffsets are fitted within 0.017 pc. Furthermore, the I_{rel} of model 6j lie between 0.46 and 1.6. Overall, model 6j provides the lowest of all models tested in the scope of this work. The simulated cuts of this model are shown in Fig. 7. In terms of a simple best fitting model, 2e is very good (2e provides the lowest ), but when explicitly asking for a reproduction of the stratification, only inhomogeneous models work. Then models 6j and 6k are much better.
5.3.7. Line profiles
Fig. 14
Line profiles of selected transitions of model 6j. The full lines show the simulated profiles, the dashed lines are Gaussian profiles with the same peak intensity as the respective profile, but with linewidths of FWHM = 2 km s^{1} and FWHM = 4 km s^{1}, matching the observed total velocity dispersions. For each transition the yoffset corresponds to the position with the highest lineintegrated intensity. 
In addition to the maps and cuts presented in Sect. 4 the KOSMAτ 3D code is capable of simulating line profiles for each individual pixel of a simulation.The beam convolution is performed by KOSMAτ 3D as a second step for the lineintegrated intensity maps. The simulation of the line profiles is based on the velocity dependent ensemble averaged line intensities and optical depths, which have been discussed in Sect. 2.3.3, and on the velocity dependent radiative transfer as discussed in Sect. 2.3.4. The line width of single clumps (σ_{j, line}, which may depend on the mass point j of the individual clump, see Eqs. (27) and (28)) in the KOSMAτ input grid, is σ_{j, line} ≈ 0.71 km s^{1} (i.e. FWHM = 1.67 km s^{1} or a Doppler broadening parameter of b = 1 km s^{1}). The ensemble velocity dispersion (σ_{j, ens}, see Eq. (26)) is an additional input/fit parameter. Both, σ_{j, line} and σ_{j, ens}, can be different for clump and interclump medium.
Here, we did not aim for a fit of the line profiles observed in the Orion Bar PDR, and have used a fixed velocity dispersion based on some observed line widths σ_{tot,j}. For the total velocity dispersion of the dense clumps we have used σ_{tot,j} ≈ 0.85 km s^{1} (FWHM = 2 km s^{1}) and for the interclump medium we applied σ_{tot,j} ≈ 1.70 km s^{1} (FWHM = 4 km s^{1}) as typical values for the Orion Bar PDR (see Nagy et al. 2013, and references therein). The clump and interclump ensemble velocity dispersion then follows from (51)Consequently, our input parameters are σ_{ens,j} ≈ 0.47 km s^{1} for the ensemble of dense clumps and σ_{ens,j} ≈ 1.54 km s^{1} for the ensemble representing the interclump medium. The systematic velocity of all voxel (see parameter v_{sys} in Eq. (26)) has been set to v_{sys} = 11.3 km s^{1}. Sampling the spectra at 21 different velocities around v_{sys}, with a spacing of 0.5 km s^{1} provides resolved profiles (see velocities v_{i} and v_{obs} in Sect. 2.3.3).
The full lines in Fig. 14 show selected line profiles from the model with the lowest , namely model 6j (see Sect. 5.3.6). The dashed lines in the figure are Gaussian line profiles with the same peak intensity as the respective profile, but with linewidths of FWHM = 2 km s^{1} and FWHM = 4 km s^{1}, corresponding to the input linewidths. By comparing the simulated profiles with the Gaussian profiles we can see that a large fraction of the [Cii] emission is emitted by the interclump medium, while the transitions of the CO isotopologues and of HCO^{+} are dominantly emitted by the dense clumps. Furthermore, some profiles appear broadened as suggested by observations (Nagy et al. 2017), however, fit and quantitative comparison are left for future work.
5.4. Discussion
We find that a geometry of the Orion Bar region similar to the geometry derived in HJ95 is well suited to reproduce the observed stratification and line intensities. Significant conclusions can be drawn, however, from our unsuccessful attempts to simultaneously fit the line intensities and the peak positions measuring the geometrical stratification of the Orion Bar PDR. Initial simulations based on a cylindrical geometry (see Appendix D) are less promising.
5.4.1. The stratification pattern
The definition of based on the measurement and modelling accuracies leads to a fit that is typically dominated by the contribution of the intensity mismatches. It seems much more difficult to tweak the model towards a fit of all line intensities within the measured accuracy than towards a fit of the observed stratification pattern. Consequently, we have aimed for two goals that are not easily unified: on the one hand we wanted to provide the best fit in the statistical sense, in other words the lowest value of ; on the other hand we wanted to reproduce the observed stratification pattern as good as possible.
The lowest , namely , and hence the best fit in the statistical sense, is provided by models 2d and 2e, which fit the lineintegrated intensities of all transitions within a factor 2.2 (the I_{rel} of the different transitions lie between 0.50 and 2.1 for model 2d and between 0.51 and 2.2 for model 2e). However, these models cannot reproduce the stratification pattern observed for the Orion Bar PDR. All models from Sects. 5.3.1 to 5.3.5 indicate that a reproduction of the stratification pattern based on a homogeneous setup (i.e. models that contain the same ensembles of clumps in each voxel; d_{clumps} = 0) is impossible.
The observable stratification in the lineintegrated intensity maps must stem from a combination of spatially varying excitation conditions, column densities, and line widths from clump and interclump medium (see Fig. 14 and also Fig. D.2) along the different lines of sight through the inclined cavity wall, modified by the beam convolution. As the homogeneous models fail to reproduce the stratification, we had to switch to models where dense clumps only exist at some depth into the cloud; d_{clumps}> 0) in Sect. 5.3.6.
This is in agreement with previous models and observations. Van der Werf et al. (1996) identify an elongated clump with a thickness of about 10″ at ~20″ from the IF into the cloud, and deduce a density increase in this region based on CS 5−4 and C^{34}S 3−2 observations and a Large Velocity Gradient (LVG) model. Parmar et al. (1991) proposed already a clumpy picture of the PDR with an increasing size and number of clumps from the IF into the molecular cloud. This was later adopted by HJ95. A detailed investigation was performed by Young Owl et al. (2000). They found that the Orion Bar is best modelled when incorporating a ridge of dense clumps into a thinner interclump medium at a depth of 20″. This is in agreement with our model fit where dense clumps had to be added to the interclump medium at a depth of 10″ to 20″, to reproduce the observed chemical stratification.
The best fit of the stratification pattern is provided by model 6l, which has , fitting the relative yoffsets within (0 ± 0.01) pc, that is in about the accuracy of the simulations, except for HCO^{+}3−2. As the HCO^{+}3−2 peak position provides a major outlier in the observational data (see Sect. 3.2), we rather speculate that the unsuccessful fit of this position might be caused by a problem in the observational data. For further improvements in the fit of the stratification pattern, based on our current method, higher resolution observations and smaller voxel sizes are necessary. We have also found models that provide good compromises between the fit of the stratification pattern and the lineintegrated intensities, overall, model 6j provides the lowest (see Sect. 5.3.6).
The parameters of the “bestfitting” models are summarised in Table 4. Our simulations show that these models can reproduce the stratification pattern, however a discrimination between the tested values for the depth at which the inset begins, d_{clumps} = 0.02, 0.03 or 0.04 pc, is not possible. The best fits of the lineintegrated intensities come from homogeneous models, or, if these are excluded, from models with d_{clumps} = 0.02 pc. The best fit of the stratification pattern comes from a model with d_{clumps} = 0.04 pc.
5.4.2. Line intensities
When using the original parameters from HJ95 (see Table 4) we find that almost all line intensities are too low compared to the observed lineintegrated intensities. Only the [Cii] intensity, which originates predominantly from the interclump medium, can be roughly reproduced by the original HJ95model. Even when considering the optically thin C^{18}O lines that were used to derive the column densities we need at least a factor 1.5 more mass per voxel than suggested by HJ95 (see Sect. 4). To enable a fit of the other lineintegrated intensities within a factor between two or three, the bestfitting models (see Sect. 5.4.1) use M_{cl,tot} = 2M_{HJ}^{15} for a depth of the cavity of 0.6 pc. Consequently, our total mass per voxel is a factor 2.1 to 2.4 higher (for M_{inter,tot} = 0.1 to 0.4 M_{HJ}; see Sect. 5.4.4) than the value inferred from HJ95.
The difference can be explained from the nature of the different models. HJ95 use a two component (clump and interclump medium) model with a uniform kinetic temperature of T_{kin} = (85 ± 30) K (for both components) to fit the observations. In the KOSMAτ PDR code, the full gas temperature distribution is calculated as a function of the clump radius. For a clump of 1 M_{⊙} with a hydrogen surface density of n_{s} = 10^{6} cm^{3} and an FUV flux at the clump surface of 10^{4}χ_{0}, the KOSMAτ PDR code computes a clumpaveraged temperature of 67 K, relatively close to the value by HJ95. However, in the KOSMAτ simulations there is a significant change in temperature between surface and core. If we compute the average temperature “felt” by a particular molecule, which is obtained when weighing the temperature profile by the abundance of the different species, we obtain very different temperatures. C^{+}, being abundant in a hot surface layer, feels an average temperature of about 1600 K while CO and ^{13}CO feel temperatures of 38 K, and HCO^{+} of about 32 K.
The emission of all optically thin molecular species is therefore significantly weaker in our model than in the 85 K model from HJ95. As the line intensities are roughly proportional to the source function, which is itself determined by the excitation temperature, we find that a change of the gas temperature from 85 K to 38 K reduces the line intensities by a factor of about 2.5 for the lowerJ CO lines. This explains the new upper limit on the molecular column density, deduced from the same C^{18}O observations in Sect. 4, compared to the model of HJ95. By using a 2.7 times higher column density than HJ95, our best fitting model (6j) overestimates the C^{18}O 3–2 lineintegrated intensity by about 25% (see Sect. 4). This is better than the deviation that we find between our fit and observations for the other CO isotopologues. Taking the constraints from the C^{18}O observations we cannot further increase the total column densities. However, all our model fits show the tendency that in particular the low and midJ lines from CO, ^{13}CO, and HCO^{+} are somewhat too weak.
The lineintegrated intensities of our bestfitting models follow a general trend, which is visible in the scatter plots, for example in Fig. 12. While for many models the fit of the [Cii] lineintegrated intensity is satisfactory, it tends to be too low for other transitions, especially for CO 2−1, 3−2 and 6−5 and for ^{13}CO 6−5 and 10−9. Even with model 3n, which provides the best fit of the lineintegrated intensities, only a factor 0.54 of the observed CO 2−1 lineintegrated intensity is reproduced (see Fig. 10). On the other side, the lineintegrated intensities of the CO 16−15 and HCO^{+}6−5 transitions are predicted too high, in our bestfitting models by a factor of about two.
The numerical experiments show that the intensity of the CO 16−15 and HCO^{+}6−5 transitions depend strongly on the FUV flux that is available for the excitation of the gas in the dense clumps. Other transitions (e.g. lowJ transitions of the CO isotopologues) are less affected. The FUV flux is governed mainly by the composition of the interclump medium. Due to the numerous lowJ line observations used within our fitting process, the models are forced into a parameter range where the interclump gas is strongly reduced, resulting in a high FUV flux within the cloud and therefore overall increased lineintegrated intensities. The effect on [Cii], which stems from both dense clumps and interclump gas, is minor, but for the sensitive highJ rotational lines, we obtain the outliers with too high predictions, that have been described above. The pattern becomes even stronger when we add very small and dense clumps, in other words when we reduce m_{l,cl}. In the opposite case, by increasing the amount of interclump medium and hence reducing the FUV flux in the cloud, we can adjust all relative intensities to similar values, in other words remove the characteristic pattern, but then all intensities (except for [Cii]) are too low. This issue asks for a new mechanism, which systematically increases the lineintegrated intensities of all, in particular the lowJ, molecular transitions.
Adding the background molecular cloud (see Sect. 4 and model 2b_ext in Table 5) increases the lineintegrated intensities of the lowJ transitions of the CO isotopologues and of HCO^{+}3−2, which can be excited by very low FUV fluxes. Consequently, the extension of the compound improves the fit of lineintegrated intensities (while the influence on the stratification pattern is small) and slightly flattens the scatter plot of the I_{rel}. The amount of background material is, however, constrained by the C^{18}O limit as discussed in Sect. 4, so that no significant change to our results is possible from this side.
An enhancement of the CO line intensities on the modelling site might be possible when including nonequilibrium effects due to an advancing IF: Störzer & Hollenbach (1998) find that in such models lowJ CO lines can be enhanced by a factor two compared to equilibrium models. The midJ CO lines can also be affected.
5.4.3. Composition of the dense clumps
The characteristic intensity pattern obtained based on the original model parameters from HJ95 (Model 1d) shows a growing discrepancy between the predicted line intensities and the observed values with the rotational level for our linear molecules. Moreover, it does not reproduce the observed stratification pattern at all. This indicates that in this setup the molecular material is too cold and too evenly distributed. As a consequence, we had to concentrate more mass in dense clumps and reduce the fraction of interclump mass to explain the observed intensities. Apart from the need for a higher total voxel mass discussed above, a reasonable fit of the integrated intensities is only possible when increasing the fraction of material that is contained in the dense clumps from the 10% in HJ95 to 83–95%.
Clump densities found in the literature cover cm^{3} (HJ95, corresponding to ρ_{cl} = 2 × 10^{6} cm^{3}), 3 × 10^{6} cm^{3} (Young Owl et al. 2000) or between 3 × 10^{6} and 1.2 × 10^{7} cm^{3} (assuming that the clumps are virialised, see Lis & Schilke 2003).
When comparing ensembleaveraged densities of ρ_{cl} = 10^{6} cm^{3} and 4 × 10^{6} cm^{3} for the dense clumps we find that the higher density slightly improve the fit of the lineintegrated intensities. Unfortunately, we could not test higher ensembleaveraged densities than 4 × 10^{6} cm^{3} due to the boundaries of the KOSMAτ parameter grid. It only provided densities up to 10^{7} cm^{3}, while a full clump spectrum with an average density of 10^{7} cm^{3} would contain some clumps with significantly higher density.
In Sect. 5.3.5, we have tested models for which the lower cutoff mass of the ensemble of dense clumps has been increased relative to the initial value of 10^{3}M_{⊙}. We notice that the small (and dense) clumps contribute to the molecular emission. Removing them, while keeping the ensembleaveraged density and total mass fixed, systematically decreases the lineintegrated intensities of all transitions (except [Cii]), especially for the highJ transitions of the CO isotopologues. As the simulated lineintegrated intensities tend to be too low in our models, the fit of the lineintegrated intensities improves if small and dense clumps are included. Consequently, our simulations do not support the “turnover” in the clumpmass function that has been suggested by some authors (see Sect. 2.2).
For model 6j the radii of the clumps at the different mass points are { R_{j} } _{j = 1...4} = { 0.0008,0.0022,0.0059,0.0159 } pc for the ensemble of dense clumps and 0.010 pc for the interclump medium. At the distance of 414 pc to Earth the size of the largest clumps (2 × 0.0159 pc) correspond to 15.8′′, which is about a factor two lager than the sizes that have been observed/derived by Lis & Schilke (2003) and Young Owl et al. (2000; see Sect. 3.2).
5.4.4. Composition of the interclump medium
The interclump medium has to account for a significant fraction of the [Cii] emission and partly for the emission of the lowJ HCO^{+}, CO and ^{13}CO lines. Its main effect is, however, the attenuation of the FUV field, effectively lowering the gas temperature in the dense clumps. Ensembles with a more homogeneous distribution of the ISM within the voxels, which is for example achieved by reducing the ensembleaveraged density while keeping the total mass constant, cause stronger FUV attenuation.
The original HJ95parameters correspond to a VFF of the interclump medium of (nearly) unity. In a homogeneous setup the combination of dense clumps (with M_{cl,tot} = 2M_{HJ}) and interclump medium provides too much FUV attenuation to allow for sufficient excitation of the (especially highJ) transitions that emit at some depth into the cloud. Based on simulations of inhomogeneous models with an interclumpVFF of unity (not shown) we conclude that if we limit ourselves to an interclumpVFF of one, we can finetune the composition of the interclump medium to optimise the fit of the stratification pattern or of the lineintegrated intensities, but we do not find a set of parameters providing both.
In the homogeneous setup the fits converges towards models that use a large amount of mass in the dense clumps, but no interclump medium (see models 1z and 1E). However, such models clearly contradict the observed stratification pattern and direct observations of the interclump medium (e.g. Stutzki et al. 1988; Ossenkopf et al. 2013). If we optimise the inhomogeneous models after dropping the constraint of the fixed interclumpVFF we find the bestfitting models with an interclumpVFF between 0.07 and 0.28, discussed in Sect. 5.4.1.
In our initial model we used clumps of 10^{2}M_{⊙} to represent the interclump medium. In Sect. 5.3.5, we varied this assumption to use clumps of 10^{3}M_{⊙}, 10^{2}M_{⊙}, and 1M_{⊙} instead. Changing the mass points has two major effects. First, the mass points yield a possibility to finetune the FUV attenuation in the cloud. Representing the interclump medium by clumps with 10^{3}M_{⊙}, which allows for a more homogeneous distribution of the ISM, increases the FUV attenuation in the cloud, degrading the quality of the fit. Second, the small clumps also contribute to the molecular emission. If we increase the size of the clumps in the interclump medium, the reduced emission from the interclump gas leads to stratification offsets that are too small compared to the observed structure. Hence, our initial guess turned out to be a good choice.
For the interclump medium densities in the range cm^{3} (HJ95, corresponding to ρ_{inter} = 6 × 10^{4} cm^{3}), 5 × 10^{4} cm^{3} (Young Owl et al. 2000) or 2 × 10^{5} cm^{3} (Simon et al. 1997) have been proposed. Our bestfitting models have a hydrogen nucleus density of 10^{5} cm^{3}. If we correct this number by the VFFs between 0.07 and 0.28 found for our best models, we obtain an average density of the interclump medium over the volume of the voxel of 7 × 10^{3} to 2.8 × 10^{4} cm^{3}, which is somewhat lower.
The Herschel observation (Fig. 3) shows that [C ii] has a rather smooth emission profile across the bar. Figure A.11 shows [C ii] emission of the interclump medium of model 6j. In this figure we have used a number surface density corresponding to 80 voxels along one line of sight, which is typical for the models discussed in this work. The figure shows that although the VFF of the interclumps medium of model 6j is only 0.04, the emission becomes rather smooth, especially if we consider that it is observed with an instrument resolution of about 0.02 pc.
5.4.5. The FUV illumination
In this work we have assumed that the FUV radiation field incident on a clump surface is isotropic. However, we have also applied the KOSMAτ 3D code to the Orion Bar PDR, where the FUV irradiation comes from one dominating source and consequently from a defined direction. Therefore, one could argue that our (isotropic) approach increases the hot PDR surface area inside the cloud leading to an overestimation of the species originating form the hot gas, for instance the highJ CO lines. However, at some depth into the cloud, the FUV photons are efficiently scattered (Stoerzer et al. 1996) and the assumption of an isotropic radiation field becomes reasonable. For the description of the surface of the Orion Bar a model using beamed illumination would be more precise.
We have started our simulations with an FUV flux at the IF of 4.4 × 10^{4}χ_{0}, adopted from HJ95. Furthermore, in Sect. 5.3.3 we have shown how increasing this values by 25% and 50% slightly improves the fit of the lineintegrated intensities, but the effect is small compared to variations of other parameters. Changes of the FUV flux due to FUV attenuation inside the cloud, which is governed by the composition of the ensembles, has a significantly stronger impact on the simulation outcome. Hence, our models cannot be used to determine the FUV flux that is incident on the cloud surface.
In Sect. 5.3.4 we have presented models with inclination angels of the bar of 0°, 3°, 7° or 15°. From investigations of the line intensities emitted by the different voxels of the model compound we find that some species emit rather locally (see for example Fig. 9). For these species increasing the inclination angle can reduce the excited column density along a line of sight through the compound, into the direction of the observer. Still, an increase of the lineintegrated intensity in the final map is possible, due to the beam convolution. In the simulations we had to compromise between these effects. For other tracers the excitation is less dependent on the FUV flux and the emission comes from a more extended region (see for example Fig. 8). For these transitions the impact of the inclination angle is smaller. The stratification pattern is produced by the rather complicated combination between FUV attenuation, inclined geometry of the bar and beam convolution. It is difficult to disentangle these effects, however, in general the pattern becomes more random, and hence harder to finetune, for large inclination angles. Our bestfitting inhomogeneous models use α = 3° or less. Such a small inclination angle was proposed by Melnick et al. (2012).
5.4.6. Further model improvements
As a solution to the overall insufficient fit of the line intensities of the combination of all tracers considered here, it might be useful to play with the isotopic abundances. A lower ^{18}O abundance could potentially fit the observed high intensities of the CO and ^{13}CO lines under the column density constraint from the C^{18}O observations. Nonequilibrium models for the PDR chemistry as proposed by Störzer & Hollenbach (1998) could also lead to an enhancement of the CO line intensities.
To deal with the relatively high mismatch of the modelled line intensities compared to the mismatch of the stratification pattern, it may be useful to create a new chisquare criterion with a higher weight of the yoffsets that allows for a better combined fit of both model aspects. Another possibility to obtain further constraints would be to fit the whole intensity profile along the cuts and not only the pixels that provide the peak intensities. A model with a more complex density structure than our idealised sharp transition might further improve our fit. Furthermore, a systematic fit of the line profiles, as discussed in Sect. 5.3.7, would provide further constraints. Important information could come for example from the fit to more complicated line profiles as, in the context of the Orion Bar, seen for different transitions of CH^{+} and SH^{+} (Nagy et al. 2013).
A further improvement of the fit based on the chisquare tests could come from a more deliberate selection of the fitted tracers (if available). For example, with the current selection, only [Cii] is sensitive to the thin and hot interclump gas at the IF. An inclusion of other transitions that are expected to peak at the IF, as for instance CH^{+} and SH^{+} (Nagy et al. 2013) or OH^{+} (van der Tak et al. 2013) could increase the weight of a correct fit of the interclump medium.
In an ideal approach we should have repeated the twodimensional scans using different sets of initial parameters and different successions for the parameter variations, to guarantee to find the globally best fit. This was, however, practically impossible due to the relatively large computational effort for each model run. In this way our fit may not be the best possible one, but our approach guarantees that we understand the effect of every individual parameter.
6. Summary
The observation of highdensity, high temperature tracers combined with a layered structure of the Orion Bar spanning over more than 15″ rules out any description of the PDR in terms of a simple planeparallel model. Photondominated regions, like molecular clouds, are clumpy and filamentary.
We propose a numerical model that is based on the representation of any PDR by an ensemble of clumps (possibly immersed in a thin interclump medium). Our new model KOSMAτ 3D builds on the KOSMAτ PDR code. It enables us to simulate the emission of star forming regions with arbitrary 3D geometries. The region is modelled using cubic voxels where each voxel is filled with clumpy structures, following a discrete mass distribution. The ensemble properties can vary between different voxels. Using a probabilistic algorithm for the calculation of the ensembleaveraged FUV extinction the incident FUV flux is derived for each voxel. A velocity dependent version of the probabilistic algorithm is used to calculate ensembleaveraged line intensities and optical depths and allows us to simulate the radiative transfer through the compound. The output of our code includes lineintegrated intensity maps and full line profiles.
As a first test of the new model we performed simulations of the Orion Bar PDR. In these simulations we tried to simultaneously reproduce the lineintegrated intensities and the spatial offsets of the emission peaks, for different transitions of CO isotopologues, HCO^{+} and [Cii]. Different geometries and parameter combinations have been tried out.
We find that a line fit is only possible if we invoke a process that removes dense clumps close to the PDR surface. The detailed stratification profile cannot be reproduced by models with a spatially constant ratio of clump to interclump gas.
The composition (i.e. the ensembleaveraged density and the total mass of the ensemble) of the interclump medium as well as the total molecular column density provided by the dense clumps are the most critical parameters for the simultaneous fit of the line intensities and the stratification structure of the Orion Bar. The interclump medium governs the FUV attenuation and hence the spatial layering while the clumps produce most of the emission from the molecular tracers. Our fit requires an ensembleaveraged density of 4 × 10^{6} cm^{3} for the ensemble of dense clumps and an ensembleaveraged density of 10^{5} cm^{3} for ensemble representing the interclump medium. Furthermore, for our best models we need a VFF of the interclump medium between 0.07 and 0.28, which indicates that also the interclump medium is not homogeneous, but breaks up into substructure.
To reproduce the observed lineintegrated intensities, a large ratio (between 5 and 20) of total clump to total interclump mass is needed. This contradicts the earlier estimates by HJ95.
The depth of the cavity and the position of the illuminating source are of minor importance, as long as the total molecular column density is fixed. Only positions of the illuminating source close (~0.1 pc) to the background molecular cloud can be excluded. Furthermore, the observations are best reproduced if a small inclination angle of the bar, in the order of 3°, is used in the model.
The focus of this work was on testing the new 3D PDR model and on fitting lineintegrated intensity maps of the Orion Bar PDR. A systematic comparison of the simulated line profiles to observations is left for future work.
The index j is related to the mass of the clump (see Sect. 2.2.2).
The grainsize distribution denoted with WD0125 in Röllig et al. (2013) from line 25 in Table 1 in Weingartner & Draine (2001a) was used.
Atomic hydrogen is only contained in a thin surface layer (A_{V} ≲ 0.1) of a PDR (see for example Röllig et al. 2007). Hence, in the comparison between simulated (column) densities and the results stated in HJ95, we assume that the contribution of atomic hydrogen is negligible, meaning that we use N ≈ 2 N_{H2} and ρ ≈ 2 n_{H2} when comparing to the molecular densities from HJ95.
The ratio between the Draine field χ_{0} and the Habing field G_{0} (both integrated over the FUV range) is χ_{0}/G_{0} ≈ 1.71 (Draine & Bertoldi 1996).
Available online: http://www.mpe.mpg.de/~martins/SED.html
Acknowledgments
We thank D. Lis for providing the CSO data and helpful comments. S. AndreeLabsch thanks the Deutsche Telekom Stiftung and the BonnCologne Graduate School of Physics and Astronomy for support by means of stipends. Modelling of irradiated molecular clouds is carried out within the Collaborative Research Center 956, subproject C1, funded by the Deutsche Forschungsgemeinschaft (DFG). Furthermore, we wish to thank the anonymous referee for his/her suggestive and detailed comments.
References
 Alves, J. F., & McCaughrean, M. J. 2002, in The Origin of Stars and Planets: The VLT View (Springer Verlag) [Google Scholar]
 Arab, H., Abergel, A., Habart, E., et al. 2012, A&A, 541, A19 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
 Asplund, M., Grevesse, N., & Sauval, A. J. 2005, in Cosmic Abundances as Records of Stellar Evolution and Nucleosynthesis, eds. T. G. Barnes, III, & F. N. Bash, ASP Conf. Ser., 336, 25 [Google Scholar]
 Bergin, E. A., Phillips, T. G., Comito, C., et al. 2010, A&A, 521, L20 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
 BernardSalas, J., Habart, E., Arab, H., et al. 2012, A&A, 538, A37 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
 Bisbas, T. G., Bell, T. A., Viti, S., Yates, J., & Barlow, M. J. 2012, MNRAS, 427, 2100 [NASA ADS] [CrossRef] [Google Scholar]
 Burton, M. G., Hollenbach, D. J., & Tielens, A. G. G. M. 1990, ApJ, 365, 620 [NASA ADS] [CrossRef] [Google Scholar]
 Choi, Y., van der Tak, F. F. S., Bergin, E. A., & Plume, R. 2014, A&A, 572, L10 [Google Scholar]
 Cuadrado, S., Goicoechea, J. R., Pilleri, P., et al. 2015, A&A, 575, A82 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
 Cubick, M. 2005, Diploma Thesis, Physikalisches Institut, University of Cologne [Google Scholar]
 Cubick, M., Stutzki, J., Ossenkopf, V., Kramer, C., & Röllig, M. 2008, A&A, 488, 623 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
 de Graauw, T., Helmich, F. P., Phillips, T. G., et al. 2010, A&A, 518, L6 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
 de Jong, T., Boland, W., & Dalgarno, A. 1980, A&A, 91, 68 [NASA ADS] [Google Scholar]
 Draine, B. T. 1978, ApJS, 36, 595 [NASA ADS] [CrossRef] [Google Scholar]
 Draine, B. T. 2011, in Physics of the interstellar and intergalactic medium (Princeton University Press), 326 [Google Scholar]
 Draine, B. T., & Bertoldi, F. 1996, ApJ, 468, 269 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
 Elmegreen, B. G., & Falgarone, E. 1996, ApJ, 471, 816 [NASA ADS] [CrossRef] [Google Scholar]
 Ferland, G. J., Porter, R. L., van Hoof, P. A. M., et al. 2013, Rev. Mexicana Astron. Astrofis., 49, 137 [Google Scholar]
 Gierens, K. M., Stutzki, J., & Winnewisser, G. 1992, A&A, 259, 271 [NASA ADS] [Google Scholar]
 Glover, S. C. O., Federrath, C., Mac Low, M.M., & Klessen, R. S. 2010, MNRAS, 404, 2 [NASA ADS] [Google Scholar]
 Goicoechea, J. R., Joblin, C., Contursi, A., et al. 2011, A&A, 530, L16 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
 Goicoechea, J. R., Teyssier, D., Etxaluze, M., et al. 2015, ApJ, 812, 75 [NASA ADS] [CrossRef] [Google Scholar]
 Graham, J. R., Herbst, T. M., Matthews, K., et al. 1993, ApJ, 408, L105 [NASA ADS] [CrossRef] [Google Scholar]
 Green, D. A. 2011, Bull. Astron. Soc. India, 39, 289 [Google Scholar]
 Habart, E., Dartois, E., Abergel, A., et al. 2010, A&A, 518, L116 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
 Habing, H. J. 1968, Bull. Astron. Inst. Netherlands, 19, 421 [Google Scholar]
 Heithausen, A., Bensch, F., Stutzki, J., Falgarone, E., & Panis, J. F. 1998, A&A, 331, L65 [NASA ADS] [Google Scholar]
 Hogerheijde, M. R., Jansen, D. J., & van Dishoeck, E. F. 1995, A&A, 294, 792 [NASA ADS] [Google Scholar]
 Hollenbach, D. J., & Tielens, A. G. G. M. 1997, ARA&A, 35, 179 [NASA ADS] [CrossRef] [Google Scholar]
 Hollenbach, D. J., & Tielens, A. G. G. M. 1999, Rev. Mod. Phys., 71, 173 [Google Scholar]
 Hollenbach, D., Kaufman, M. J., Neufeld, D., Wolfire, M., & Goicoechea, J. R. 2012, ApJ, 754, 105 [NASA ADS] [CrossRef] [Google Scholar]
 Howe, J. E., Jaffe, D. T., Genzel, R., & Stacey, G. J. 1991, ApJ, 373, 158 [NASA ADS] [CrossRef] [Google Scholar]
 Jansen, D. J., Spaans, M., Hogerheijde, M. R., & van Dishoeck, E. F. 1995, A&A, 303, 541 [NASA ADS] [Google Scholar]
 Kamp, I., Tilling, I., Woitke, P., Thi, W.F., & Hogerheijde, M. 2010, A&A, 510, A18 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
 Koester, B., Stoerzer, H., Stutzki, J., & Sternberg, A. 1994, A&A, 284, 545 [NASA ADS] [Google Scholar]
 Kramer, C., Stutzki, J., Rohrig, R., & Corneliussen, U. 1998, A&A, 329, 249 [NASA ADS] [Google Scholar]
 Langer, W. D., & Penzias, A. A. 1990, ApJ, 357, 477 [NASA ADS] [CrossRef] [Google Scholar]
 Le Bourlot, J., Le Petit, F., Pinto, C., Roueff, E., & Roy, F. 2012, A&A, 541, A76 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
 Levrier, F., Le Petit, F., Hennebelle, P., et al. 2012, A&A, 544, A22 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
 Lis, D. C., & Schilke, P. 2003, ApJ, 597, L145 [NASA ADS] [CrossRef] [Google Scholar]
 Mangum, J. G. 1993, PASP, 105, 117 [NASA ADS] [CrossRef] [Google Scholar]
 Marconi, A., Testi, L., Natta, A., & Walmsley, C. M. 1998, A&A, 330, 696 [NASA ADS] [Google Scholar]
 Martin, H. M., Hills, R. E., & Sanders, D. B. 1984, MNRAS, 208, 35 [NASA ADS] [CrossRef] [Google Scholar]
 Martins, F., Schaerer, D., & Hillier, D. J. 2005, A&A, 436, 1049 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
 Melnick, G. J., Tolls, V., Goldsmith, P. F., et al. 2012, ApJ, 752, 26 [NASA ADS] [CrossRef] [Google Scholar]
 Menten, K. M., Reid, M. J., Forbrich, J., & Brunthaler, A. 2007, A&A, 474, 515 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
 Mertens, M. 2013, Bachelor’s Thesis, Universität zu Köln, Physikalisches Institut, Cologne, Germany [Google Scholar]
 Müller, H. S. P., Thorwirth, S., Roth, D. A., & Winnewisser, G. 2001, A&A, 370, L49 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
 Müller, H. S. P., Schlöder, F., Stutzki, J., & Winnewisser, G. 2005, J. Mol. Struct., 742, 215 [NASA ADS] [CrossRef] [Google Scholar]
 Nagy, Z., Van der Tak, F. F. S., Ossenkopf, V., et al. 2013, A&A, 550, A96 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
 Nagy, Z., Ossenkopf, V., Van der Tak, F. F. S., et al. 2015, A&A, 578, A124 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
 Nagy, Z., Choi, Y., OssenkopfOkada, V., et al. 2017, A&A, in press DOI: 10.1051/00046361/201628916 [Google Scholar]
 Offner, S. S. R., Clark, P. C., Hennebelle, P., et al. 2014, Protostars and Planets VI, 53 [Google Scholar]
 Ossenkopf, V., Trojan, C., & Stutzki, J. 2001, A&A, 378, 608 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
 Ossenkopf, V., Rollig, M., Cubick, M., & Stutzki, J. 2007, in Molecules in Space and Laboratory, eds. J. L. Lemaire, & F. Combes, 95 [Google Scholar]
 Ossenkopf, V., Röllig, M., Neufeld, D. A., et al. 2013, A&A, 550, A57 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
 Parmar, P. S., Lacy, J. H., & Achtermann, J. M. 1991, ApJ, 372, L25 [NASA ADS] [CrossRef] [Google Scholar]
 Pellegrini, E. W., Baldwin, J. A., Brogan, C. L., et al. 2007, ApJ, 658, 1119 [NASA ADS] [CrossRef] [Google Scholar]
 Pellegrini, E. W., Baldwin, J. A., Ferland, G. J., Shaw, G., & Heathcote, S. 2009, ApJ, 693, 285 [NASA ADS] [CrossRef] [Google Scholar]
 PérezBeaupuits, J. P., Wiesemeyer, H., Ossenkopf, V., et al. 2012, A&A, 542, L13 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
 Pety, J., Goicoechea, J. R., Gerin, M., et al. 2007, in Molecules in Space and Laboratory, eds. J. L. Lemaire, & F. Combes, 13 [Google Scholar]
 Pilbratt, G. L., Riedinger, J. R., Passvogel, T., et al. 2010, A&A, 518, L1 [Google Scholar]
 Pilleri, P., Montillaud, J., Berné, O., & Joblin, C. 2012, A&A, 542, A69 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
 Press, W. H., Teukolsky, S. A., Vetterling, W. T., & Flanney, B. P. 1992, Numerical Recipes in Fortran 77: The Art of Scientific Computing, 2nd edn. (New York: Cambridge University Press) [Google Scholar]
 Roelfsema, P. R., Helmich, F. P., Teyssier, D., et al. 2012, A&A, 537, A17 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
 Röllig, M., Ossenkopf, V., Jeyakumar, S., Stutzki, J., & Sternberg, A. 2006, A&A, 451, 917 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
 Röllig, M., Abel, N. P., Bell, T., et al. 2007, A&A, 467, 187 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
 Röllig, M., Szczerba, R., Ossenkopf, V., & Glück, C. 2013, A&A, 549, A85 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
 Simon, R., Stutzki, J., Sternberg, A., & Winnewisser, G. 1997, A&A, 327, L9 [NASA ADS] [Google Scholar]
 SimónDíaz, S., & Stasińska, G. 2011, A&A, 526, A48 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
 Stahl, O., Wade, G., Petit, V., Stober, B., & Schanne, L. 2008, A&A, 487, 323 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
 Sternberg, A., & Dalgarno, A. 1989, ApJ, 338, 197 [NASA ADS] [CrossRef] [Google Scholar]
 Stoerzer, H., Stutzki, J., & Sternberg, A. 1996, A&A, 310, 592 [Google Scholar]
 Störzer, H., & Hollenbach, D. 1998, ApJ, 495, 853 [NASA ADS] [CrossRef] [Google Scholar]
 Stutzki, J., & Guesten, R. 1990, ApJ, 356, 513 [NASA ADS] [CrossRef] [Google Scholar]
 Stutzki, J., Stacey, G. J., Genzel, R., et al. 1988, ApJ, 332, 379 [NASA ADS] [CrossRef] [Google Scholar]
 Stutzki, J., Bensch, F., Heithausen, A., Ossenkopf, V., & Zielinsky, M. 1998, A&A, 336, 697 [NASA ADS] [Google Scholar]
 Szczerba, R., Omont, A., Volk, K., Cox, P., & Kwok, S. 1997, A&A, 317, 859 [NASA ADS] [Google Scholar]
 Tielens, A. G. G. M., & Hollenbach, D. 1985, ApJ, 291, 722 [NASA ADS] [CrossRef] [Google Scholar]
 Tielens, A. G. G. M., Meixner, M. M., van der Werf, P. P., et al. 1993, Science, 262, 86 [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
 van der Tak, F. F. S., Nagy, Z., Ossenkopf, V., et al. 2013, A&A, 560, A95 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
 van der Werf, P. P., Stutzki, J., Sternberg, A., & Krabbe, A. 1996, A&A, 313, 633 [NASA ADS] [Google Scholar]
 van der Werf, P. P., Goss, W. M., & O’Dell, C. R. 2013, ApJ, 762, 101 [NASA ADS] [CrossRef] [Google Scholar]
 van der Wiel, M. H. D., van der Tak, F. F. S., Ossenkopf, V., et al. 2009, A&A, 498, 161 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
 Visser, R., van Dishoeck, E. F., & Black, J. H. 2009, A&A, 503, 323 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
 Walmsley, C. M., Natta, A., Oliva, E., & Testi, L. 2000, A&A, 364, 301 [NASA ADS] [Google Scholar]
 Weingartner, J. C., & Draine, B. T. 2001a, ApJ, 548, 296 [NASA ADS] [CrossRef] [Google Scholar]
 Weingartner, J. C., & Draine, B. T. 2001b, ApJS, 134, 263 [NASA ADS] [CrossRef] [MathSciNet] [Google Scholar]
 Wen, Z., & O’dell, C. R. 1995, ApJ, 438, 784 [NASA ADS] [CrossRef] [Google Scholar]
 Yorke, H. W. 1980, A&A, 86, 286 [NASA ADS] [Google Scholar]
 Young Owl, R. C., Meixner, M. M., Wolfire, M., Tielens, A. G. G. M., & Tauber, J. 2000, ApJ, 540, 886 [NASA ADS] [CrossRef] [Google Scholar]
Appendix A: Testing the probabilistic approach
In Sect. 2.3.3 ensembleaveraged line intensities and optical depths have been derived based on binomial distributions (referred to as probabilistic approach). This formalism was verified using a direct approach that calculates the ensembleaveraged quantities for a specific realisation of an ensemble. Here, the direct approach is presented for the [Cii], CO 3−2 and CO 16−15 lines. As it requires a relatively large amount of computing time (the calculation of the ensembleaveraged line intensity and optical depth of one “small” map with 1184 × 1184 pixel, for instance Fig. A.5, takes about two hours) it is not used in the simulations of the KOSMAτ 3D code, where ensembleaveraged quantities need to be calculated for about 10^{5} different ensembles. However, we used it to calculate the ensembleaveraged quantities of selected ensembles with known parameters to be compared to the results of the probabilistic approach. This serves three purposes:
Firstly, to verify that the ensembleaveraged quantities calculated with the probabilistic approach match the averaged quantities of real random realisations (random positions for each clump) of the ensemble.
Secondly, to verify that averaging over the clump projected area (see Sect. 2.1) can be used (the method presented here accounts for the full (nonaveraged) profiles I_{line}(p) and τ_{line}(p) with p being the impact parameter, or radial distance from the centre point of the clump).
Thirdly, to understand how much the ensembleaveraged quantities of the same ensemble can vary depending on the random positions of the individual clumps.
As discussed in Sect. 2.3.1 we consider randomly distributed clumps of masses M_{j} with a fixed number surface density N_{j}/ (Δs)^{2}. The projected surfaces of the clumps may overlap. We focus on the discussion of one ensemble representing dense clumps and in addition, for the 1.9 THz [Cii] transition, we show results of one typical ensemble representing the interclump medium (see below). The “denseclump” ensemble tested in this appendix contains clumps with masses { M_{j} } _{j = 1...nM} = { 10^{3},10^{2},10^{1},10^{0} }M_{⊙} and a total mass of 0.00346 M_{⊙} per (0.01 pc)^{2} projected surface area (which translates into N_{j}/ (Δs)^{2} using Eq. (19)). The ensemble averaged density has been fixed to be 4 × 10^{6} cm^{3}, the averaged densities of individual clumps have been calculated using Eq. (18).
As discussed in Sect. 2.3.1 a clumpy ensemble can be scaled to arbitrary numbers of clumps as long as the number surface density for each mass point, , is kept constant. For the direct approach we have created squared maps of a size (Δs′)^{2} in which the (centre points of the) clumps are randomly distributed. Two different map sizes are used in this appendix, small maps contain one and large maps contain two clumps at mass point 1 M_{⊙}. In the maps we ignore an area of the thickness R_{cl,max}/ 2 around the edges because that region could be affected by clumps with centres outside of the map, that are no taken into account here.
Maps of random representations of the ensemble of dense clumps are shown in Figs. A.1, A.2, A.5 and A.7. For all presented maps an FUV flux of 10^{4}χ_{0} has been used. For instance, the colour scale of Fig. A.1 shows the line centre intensity of the [Cii] line, summed up along lines of sight perpendicular to the printed surface. [Cii] emission is strongest in the outer layers of the clumps. It is affected by limb brightening. The resulting intensity profile I_{line}(p) (see Eq. (5)), where the [Cii] line intensity is highest at the edge of each (sufficiently large) projected clump, is clearly visible in Fig. A.1. Analogously to Fig. A.1 maps showing the optical depth have been simulated. For example Fig. A.2 shows the same map as Fig. A.1 but with the colour scale giving the optical depth of the [Cii] transition.
Fig. A.1
Large map (see text) showing one representation of the test ensemble, consisting of randomly distributed clumps. The colour scale gives the line centre intensity of the 1.9 THz [Cii] transition where the line intensity is highest in the outer layer of each clump. This maps shows the 22nd representation from Fig. A.3. The ensemble is shown in a superpixel, in other words with the area filling factor of a normal 0.01 pc pixel, but scaled in the lateral directions to contain enough clumps of each size. 
Fig. A.2
Same as Fig. A.1 but with the colour scale giving the optical depth of the 1.9 THz [Cii] transition. 
Fig. A.3
Ensembleaveraged [Cii] line centre intensities for different realisations of the test ensemble. The circles show the ensembleaveraged intensities of 20 different small intensity maps and the squares give the values for ten different large intensity maps as presented in Fig. A.1. The black line gives the mean value of the 30 maps, weighted by the respective number of grid points. For the 20 small maps the average is (2.24 ± 0.50) K and for the ten large maps it is (2.17 ± 0.33) K. The stated error is the standard deviation, which, as expected, is larger for the smaller maps. The red, dashed line gives the value derived using the probabilistic approach for the same ensemble. The difference between the two approaches is about 2.6%. 
Fig. A.4
Same as Fig. A.3 but plotted for the ensembleaveraged optical depths, τ, of the [Cii] line. For the [Cii] line the difference between the probabilistic approach and the average over the 30 results derived with the direct approach is about 2.6% for line intensities and optical depths. 
Fig. A.5
Small map of one representation of the test ensemble. The colour scale gives the optical depth of the CO 3−2 line (line centre), which is highest for lines of sight through the dense cloud cores. This map shows the first realisation from Fig. A.6. The increasing optical depth towards the clump centres provide the visual impression that the clumps are smaller than their actual size. 
Fig. A.6
Ensembleaveraged optical depths of the CO 3−2 line. The circles show the results for ten different realisations of the ensemble as presented in Fig. A.5. The black line gives the mean value of the ten maps, namely (0.038 ± 0.006) where 0.006 is the standard deviation. The red, dashed line gives the value derived with the probabilistic approach for the same ensemble. The difference between the two approaches is about 3.7% (and about 3.5% for the line intensities which are not shown). 
Fig. A.7
Small map showing the line centre intensity of the CO 16–15 line (colour scale). This map corresponds to the 5th representation from Fig. A.8 where the large clump is sitting at the lower edge, causing a small ensembleaveraged intensity and optical depth. 
Fig. A.8
Ensembleaveraged intensities of the CO 16–15 line (line centre). The circles show the results for ten different realisations of the ensemble as presented in Fig. A.7. The black line gives the mean value of the ten maps, namely (7.6 ± 1.0) K where 1.0 is the standard deviation. The red, dashed line gives the value derived by the probabilistic approach for the same ensemble. Here, the two lines are lying on top of each other (the difference between the two approaches is about 0.004% for the line intensities and about 0.8% for the optical depths which are not shown here). 
Fig. A.9
Intensity map showing a representation of the interclump medium with initially 100 identical clumps (a few clumps have been cut away during the removal of the edges of the map). The colour scale gives the line centre intensities of the [Cii] line. 
Fig. A.10
Ensembleaveraged [Cii] line centre intensities of different realisations of the interclumpensemble. The circles show the ensembleaveraged intensities of ten different maps containing ten clumps and the squares give the values for ten different maps with 100 clumps as presented in Fig. A.9. The black line gives the mean value of the 20 maps, weighted by the respective number of grid points. For the maps with ten clumps the average is (24.4 ± 7.1) K and for the maps with 100 clumps it is (23.6 ± 1.0) K, where the stated error is the standard deviation. The red, dashed line gives the value derived by the probabilistic approach (for N_{nM = 1} = 100 clumps) for the same ensemble. The two results agree within 0.4% (intensities) and 0.8% (optical depths, not shown). 
Fig. A.11
Map showing the interclump medium of model 6j, with a surface density corresponding to the full line of sight of the model provided by 80 voxels. The colour scale shows 1−Exp(−τ_{CII}). 
Each map has been derived on a spatial grid with a gridspacing d_{grid} ≪ Δs′ (see Sect. 2.3.1). The large maps, for instance Fig. A.1, contain n_{grid} = 1741 × 1741 grid points while the small maps (see Figs. A.5 and A.7) contain n_{grid} = 1184 × 1184 grid points. For each map the ensembleaveraged quantities were calculated using where the index k denotes different gridpoints. The resulting ⟨ I ⟩ _{grid} and ⟨ e^{− τ} ⟩ _{grid} can be compared to the results from the binomial approach (Eqs. (A.4) and (A.5)).
In the presented comparison we account for only one velocity bin with centrevelocity v_{i} = v_{1} = v_{sys}. This velocity bin contains all clumps of the ensemble and Eq. (26) simplifies to (A.3)Furthermore, due to v_{i} = v_{obs}, Eqs. (27) and (28) simplify to giving the linecentre intensity and optical depth for each combination of clumps. As we do not have to sum up contributions from different centrevelocities, the ensembleaveraged quantities are provided by Eqs. (32) and (33), for i = 1. These are compared to the results from the probabilistic approach.
For the [Cii] line the ensembleaveraged line intensity and optical depth have been derived for 30 different realisations of the ensemble including 20 small and ten large maps. The results are summarised in Figs. A.3 and A.4 for the intensities and optical depths, respectively. As expected, the averaged values calculated for single realisation in Figs. A.3 and A.4 show some scatter, which is larger for the smaller maps (this effect is better visible in Fig. A.10 where the sizedifference between the maps is larger). The scatter shows us how much single representations of the ensemble, which might exist in molecular clouds, can differ from the mean value derived with the probabilistic approach. The black lines give the line intensity or optical depth averaged over the 30 results of the individual representations. Within this calculation the maps got different weights, proportional to their respective sizes. The red, dashed lines show the ensembleaveraged quantities for the same ensemble, calculated using the probabilistic approach. For [Cii] the two approaches agree within 2.6% for line intensities and optical depths.
The reliability of the approach presented here depends on the size of the calculated maps and on the number of representations of the ensemble to be averaged over. However, a statistical difference remains between simulating one very large map or several smaller maps with the same total size. This difference can be understood from the different [Cii] maps: while for the large maps with (for example) N_{nM} = 2 a clump at the highest mass point can overlap with a clump of the same kind, this overlap is not possible for the small maps with N_{nM} = 1. However, Fig. A.3 shows that this difference is negligible for the dense ensembles in this work, due to their small area filling factors. For the interclump medium, which has a higher area filling factor, the situation is different (see below).
For each CO line ten small maps have been analysed. Figure A.5 shows such a map with the optical depth of the CO 3−2 transition given on the colour scale. As expected the CO 3−2 optical depth is highest for lines of sight intersecting with the dense cloud cores. The related statistical overview is shown in Fig. A.6. We find that the two approaches agree within 3.7% (3.5%) for the optical depths (line intensities). Possibly, increasing the number of samples could improve this result.
For the CO 16−15 line we present an intensity map in Fig. A.7 and the related statistical overview in Fig. A.8. The map (Fig. A.7) shows the 5th realisation of the ensemble from Fig. A.8. Here, the large clump is sitting at the lower edge of the map and is partly cut away causing the small ensembleaveraged intensity (and optical depth). However, this situation is part of the normal statistics. For CO 16−15 the agreement between the two approaches is excellent (which is coincidental due to the rather low number of sample maps), we find deviations between the two approaches of 0.004% and 0.8% for line intensities and optical depths, respectively.
In addition to the ensemble of dense clumps we have tested one typical “interclumpensemble”. This ensemble contains clumps at one mass point (10^{2}M_{⊙}), a total ensemble mass of 0.5 × 0.00173M_{⊙} per (0.01 pc)^{2} projected surface area, and an ensemble averaged density of 1.91 × 10^{4} cm^{3}. It has an area filling factor (N_{1}π(R_{1})^{2}/ (Δs)^{2}, i.e. not accounting for the fact that clumps do overlap) of about 0.8. The clumps are lying on a gridpoint of the KOSMAτ model grid, making interpolations unnecessary. One representation of this ensemble is shown in Fig. A.9 where the colour scale gives the [Cii] line (centre) intensity. The map originally contained 100 identical clump, a few clumps have been cut away during the removal of the edges of the map. One should note that for this ensemble the clumps at mass point 10^{2}M_{⊙} are about a factor six larger compared to the previous ensemble, due to the reduced density. The [Cii] intensities of different realisations of the ensemble, for maps that contained initially 10 or 100 clumps, are shown in Fig. A.10. The comparison to the probabilistic approach shows that the deviation between the two results lies below 1% for line intensities and optical depths. For the interclump medium the probabilistic approach yields an ensembleaveraged [Cii] line centre intensity of 23.68 K for N_{nM = 1} = 10 and of 23.57 K for N_{nM = 1} = 100.
The excellent agreement between the two approaches for the interclump ensemble indicates that averaging over the projected surface (see Sect. 2.1) hardly introduces any error. Furthermore, the interpolation between the line profiles I_{line}(p) and τ_{line}(p), as needed for the ensemble of dense clumps during the analysis with the direct approach, does not cause large deviations. All compared results are found to agree within the statistical scatter, which is quantified by the standard deviations of the scatter of the ensembleaveraged quantities, calculated with the direct approach.
In addition to the ensembles used for the statistical analysis in this section, we have added Fig. A.11, which shows clumps based on the parameters of the interclump medium of model 6j (see Table 5), with a number surface density corresponding to 80 voxels along a line of sight. This figure is further discussed in Sect. 5.4.4.
Appendix B: Numerical handling of the radiative transfer
A solution of the equation of radiative transfer, based on linear approximations of the emission and absorption coefficient, has been presented in Sect. 2.3.4. Here, we discuss the numerical handling of the integral in Eq. (40) in the KOSMAτ3D code. The numerical integrations in this section were performed using Wolfram Mathematica^{16}. Before we solve the integral in Eq. (40) we need to distinguish three different cases:

Case 1).
If no absorption takes place betweentwo pixels, that is k_{0} = 0 and k_{1} = 0, the equation of radiative transfer (Eq. (37)) reduces to(B.1)and integration between 0 and Δs yields (B.2)where I_{bg} indicates the incident background emission. This case (i.e. an infinite source function and hence an infinite excitation temperature) cannot occur in any physical source. However, in the code it is needed if there are “holes” in the setup in other words if voxels on a line of sight are not occupied by an ensemble (at a specific velocity) or for artificial sources which have been constructed in a way that voxels emit at a specific frequency without absorbing it. Practically, in the code, the condition  k_{0} Δs  < 10^{10} and k_{1} = 0 has been used for this case, avoiding errors due to numerical inaccuracies.

Case 2).
If the absorption coefficient does not change between two pixels, meaning that k_{1} = 0 but k_{0} ≠ 0, Eq. (40) reduces to (B.3)and numerical integration yields (B.4)Practically, in the code, the condition k_{0}> 10^{3}k_{1}Δs has been used for this case.

Case 3).
The third case is the general case which is always used if k_{1} ≠ 0. Numerical integration of Eq. (40) yields (B.5)with (B.6)where erfi(...) denotes the imaginary Error Function. is further discussed below.
Implementation, runtime and precision of the imaginary error functions in Eq. (B.6) are problematic for large function values. In the code this is avoided by the following rearrangements and substitutions:

The function , Eq. (B.6), can be written as(B.7)with (B.8)

The function has been constructed in a way that (subtractions between) large numbers are avoided. It is different for k_{1}> 0 and k_{1}< 0. Furthermore, it can be approximated for large and small function values. For k_{1}> 0 it is given by (B.9)for k_{1}< 0 (and consequently imaginary a and b) it is given by (B.10)
For 0.01 ≤  x  ≤ 8.0 both functions, Eqs. (B.9) and (B.10), have been tabulated. The code interpolates linearly between the tabulated values. Maser lines (k_{0} + k_{1}Δs< 0, i.e. x = + i  x ) are treated in linear approximation. Hence, the code should not be used for strong maser lines. For all other x, including weak maser lines (x = + i  x  with  x  < 0.1), the relative error made by interpolation or approximation of is less than one percent.
Appendix C: FUV flux at the ionisation front
Jansen et al. (1995) state that the radiation field incident on the Orion Bar corresponds to an enhancement over the average interstellar radiation field, χ_{0}, of a factor ≈4.4 × 10^{4}. Other authors give similar values, Marconi et al. (1998) estimate a flux of 1−3 × 10^{4} times the average interstellar field. Arab et al. (2012), Young Owl et al. (2000), Walmsley et al. (2000) used 1−4 G_{0} at the IF with G_{0} being the Habing field (Habing 1968)^{17}.
Here, we have reestimated the FUV flux at the IF, originating from Θ^{1} Ori C, based on synthesised stellar spectra provided by Martins et al. (2005)^{18}. Different spectra from their sample have been investigated, for stars having effective temperatures between 35 000 and 39 540 K which covers the spectral classes from O7V to O6V. Different authors (Stahl et al. 2008; Pellegrini et al. 2009; Arab et al. 2012) obtained varying results for the spectral class of Θ^{1} Ori C, all falling in the range between O6 and O7.
The selected spectra have been integrated in the FUV range, 2066 to 911 Å (6 to 13.6 eV), and the resulting flux at the position of the Orion Bar has been computed. For this calculation, the distance between Θ^{1} Ori C and the Orion Bar has been assumed to be 0.223 pc, in other words equal to the projected distance (neglecting a possible offset in radial direction which is not precisely known, see Sect. 3.1). The calculated fluxes fall between 17 and 373 erg s^{1} cm^{2} (0.63 × 10^{4}χ_{0}−13.8 × 10^{4}χ_{0}), covering the range of values discussed above.
The FUV flux representative for an O6.5 star (T_{eff} = 36 826 K, Martins et al. 2005) is found to be 38 erg s s^{1} cm^{2} or 1.4 × 10^{4}χ_{0} at the IF. The flux of 4.4 × 10^{4}χ_{0} from HJ95 is best reproduced by the star from the sample with T_{eff} = 37 760 K, which indicates the spectral class O6.5V (Martins et al. 2005). The calculated FUV fluxes make strong FUV absorption inside the Hii region between star and PDR improbable in agreement with the lack of dust observed in the cavity. Dust must have been blown out by the strong stellar winds.
Appendix D: Cylindrical models
Fig. D.1
Simulated cuts perpendicular to the Orion Bar, based on model Cyl. 5 (see Table D.1). Each colour scales gives the lineintegrated intensity of the transitions indicated above the respective cut. 
Fig. D.2
Cut through the cylindrical Orion Bar model Cyl. 4. For each voxel the colour scale gives the [Cii] line intensity of dense clumps and interclump medium, at the line centre (at 11.3 km s^{1}). The illuminating star Θ^{1} Ori C is located at [0,22.3,30]. 
Fig. D.3
Same as Fig. D.2 but plotted for the outer line wing (at 8.3 km s^{1}). Effectively, only the interclump medium contributes at this velocity. 
As discussed in Sect. 4 we have also tested models where the Orion Bar has a cylindrical shape (see Figs. D.2 and D.3). The tests of this geometry are based on the assumption that the sets of parameters that provide the best simulation results for the HJ95 geometry are also good initial guesses for the cylindrical model. We have used a cylindrical model with a deep cavity, d_{cavity} = 0.6 pc, and hence a radius for the cylinder of 0.3 pc. The centre point of the cut through the cylinder is [0,−30,30] and the illuminating source is located at [0,22.3,30] (see Fig. D.2). The inclination parameter α′ is not needed for a cylinder. Table D.1 gives an overview over some cylindrical models. The second column of the table refers to the corresponding model from Table 5 for the remaining parameters, in other words to the composition of the clump ensembles, I_{UV} and d_{clumps}.
Overview over cylindrical models.
Figures D.2 and D.3 show cuts through the cylindrical model Cyl. 4, which is an inhomogeneous model with d_{clumps} = 0.02 pc. The colour scales give the [Cii] line intensity emitted by the dense clumps and the interclump medium of the respective voxel at a specific velocity, Fig. D.2 shows the linecentre intensity (at 11.3 km s^{1}) and Fig. D.3 the emission in the line wing (at 8.3 km s^{1}). Figure D.2 illustrates that at the line centre velocity the [Cii] emission is dominated by the dense clumps, that only start two voxels below the cloud surface. Due to the higher velocity dispersion of the interclump medium (see Sect. 5.3.7), the 8.3 km s^{1} channel in Fig. D.3 is dominated by the [Cii] emission from the interclump medium, providing the highest line intensities in the voxels that are closest to the illuminating source. The observable stratification pattern follows from the radiative transfer and the beam convolution of these pictures.
In general, for cylindrical models the yoffsets where the lineintegrated intensity peaks for the different transitions are shifted deeper into the cloud (into the negative ydirection) compared to the corresponding cavitywall models. For all models listed in Table D.1 the [Cii] lineintegrated intensity peak (i.e. the reference positions for the yoffsets) is shifted by one to three pixels deeper into the PDR compared to the cavitywall model with the same parameters. As an example Fig. D.1 shows the simulated cuts of model Cyl. 5. Here the [Cii] peak lies at y = −0.08 pc, while the peak appears at y = −0.06 pc in model 6j. Hence, to provide a stratification pattern, the yoffsets of all other transitions need to be shifted even deeper into the cloud in the cylindrical models. This is hardly observed. The line intensity is dominated by the column density along the line of sight given by the cylindrical geometry, less by changing composition or excitation conditions. For the cylinder the lines of sight through the compound close to y = 0 are shorter than the lines of sight through the cloud material in the cavitywall setup (assuming small α′). Therefore, the intensity peaks are shifted from the edge of the cloud to positions where the lines of sight through the compound and hence the column densities are larger. This leads to the higher yoffsets in the cylindrical model.
Depending on the specific model setup, the can decrease or increase when we switch from the cavitywall models to the cylindrical geometry. However, this comparison is ambitious, as the of the HJ95models also depend on the inclination angle α′. Similar to the HJ95models we find that the fit of the stratification pattern is better when an inhomogeneous model (i.e. models Cyl. 4 to Cyl. 7) is used, compared to the homogeneous setups (models Cyl. 1 to Cyl. 3). None of the homogeneous models can reproduce any stratification for the CO 10−9 transition. Models Cyl. 5, 6 and 8 do provide peak positions Δy_{i}< 0 relative to the [Cii] peak (see Sect. 5.2) for all transitions. However, the Δy_{i} scatter around the observed values, complicating the finetuning of the stratification pattern. None of the tested models reaches the of our best HJ95models.
With the parameters from Table 5 the cylindrical geometry provides always systematically lower lineintegrated intensities for all transitions than the cavitywall model, resulting in a deteriorated fit (in terms of the ). This reduction results from a combination of two effects: (a) shorter lines of sight and hence reduced column densities at the front of the cylinder; (b) less FUV flux available for the excitation when the peaks are shifted deeper into the cloud (see above). This already shows the fundamental problem of cylindrical models.
Consequently, we find that the fits based on the tested cylindrical model do not reach the quality (in terms of the ) of the HJ95 models. Of course these results could be improved if stepbystep finetuning of the parameters is performed, however, finetuning of the stratification patter is expected to be difficult due to the scattering of the Δy_{i} of the different transitions. An improvement of the fit of the lineintegrated intensities might be possible if the mass per voxel in the dense clumps is drastically increased, leading to larger molecular column densities close to y = 0. A cylinder with less curvature might also provide better results.
All Tables
Summary of averaged integrated intensities and spatial offsets of the observations.
All Figures
Fig. 1
3D compound replicating a possible geometry (model 1m, see Table 5) of the Orion Bar PDR. Each cube represents one voxel filled with at least one clumpy ensemble. Coordinates are given in voxel sizes, corresponding to 0.01 pc. The colour scale (Green 2011) shows the impinging FUV flux, calculated for each voxel, for a FUV source located at [0,22.3,30]. The direction to Earth corresponds to the positive z direction. 

In the text 
Fig. 2
Faceon/edgeon/faceon Orion Bar geometry as proposed by HJ95. Values are taken from: green: HJ95; red: Menten et al. (2007); blue: Pellegrini et al. (2009); and orange: van der Werf et al. (2013). For the inclination angle, α′, values between less than 3° and 15° have been discussed (Jansen et al. 1995; Melnick et al. 2012). 

In the text 
Fig. 3
Orion Bar observed with HIFI/Herschel. The green contours show [Cii] line intensities integrated between 7 and 13 km s^{1}. The contours range between 200 and 800 K km s^{1} in steps of 100 K km s^{1}. The colour scale gives the ^{13}CO 10−9 line intensity, integrated between 9 and 12 km s^{1}. The reference position is the CSO reference position (5^{h}35^{m}20.122^{s}, −5°25′21.96′′). 

In the text 
Fig. 4
[Cii] integrated intensity (same as the contours in Fig. 3, but rotated by −145°). In this orientation Θ^{1} Ori C in the northwest of the bar is at the bottom of the map. The green line marks the cut with the highest averaged lineintegrated intensity, including all pixels with xoffsets between −11.3′ and −43.5′. 

In the text 
Fig. 5
Simulated map of lineintegrated CO 3−2 emission of the Orion Bar PDR, based on model 1m (see Table 5). The coordinates are given in units of pixels. One pixel corresponds to 0.01 pc or 5″ on the sky. The illuminating star Θ^{1} Ori C is located at x = 0 and y = 22.3 on top of the map. 

In the text 
Fig. 6
Same as Fig. 5 but after convolution with a Gaussian beam of 21.9″ or 4.4 pixels FWHM (see Table 2). 

In the text 
Fig. 7
Simulated cuts perpendicular to the Orion Bar, based on model 6j (see Table 5). Each colour scale gives the lineintegrated intensity of the transition indicated above the respective cut. 

In the text 
Fig. 8
Cut through the Orion Bar model 2b. For each voxel the colour scale gives the CO 2−1 line intensity of dense clumps and interclump medium, at the line centre (at 11.3 km s^{1}). The illuminating star Θ^{1} Ori C is located at [0,22.3,30]. 

In the text 
Fig. 9
Same as Fig. 8 but with the CO 16−15 line intensity given on the colour scale. 

In the text 
Fig. 10
Scatter plot of the lineintegrated intensities for selected models from Sects. 5.3.1, 5.3.3 and 5.3.4. For each transition the ratio between simulated and observed lineintegrated intensity at the respective peak position, I_{rel,i} = I_{fit,i}/I_{obs,i}, is plotted on a logarithmic scale. The different transitions are indicated on the abscissa and different symbols mark the different models. 

In the text 
Fig. 11
Scatter plot of the yoffsets of selected models from Sects. 5.3.1, 5.3.3 and 5.3.4. For each transition the difference between the offset of the simulated and the observed peak position, as defined in Eq. (45), is plotted. All offsets are relative to the [Cii] peak position, hence, for the reference [Cii] transition y_{diff} is always zero. A negative offset indicates that the simulated emission peak is shifted too far into the direction of Θ^{1} Ori C, a positive offset indicates that the emission appears too deep in the cloud. The different transitions are indicated on the abscissa and different symbols mark different models. 

In the text 
Fig. 12
Same as Fig. 10, plotted for selected models from Sects. 5.3.5 and 5.3.6. In addition, model 2e from Sect. 5.3.3 is given for comparison. 

In the text 
Fig. 13
Same as Fig. 11, plotted for selected models from Sects. 5.3.5 and 5.3.6. In addition, model 2e from Sect. 5.3.3 is given for comparison. 

In the text 
Fig. 14
Line profiles of selected transitions of model 6j. The full lines show the simulated profiles, the dashed lines are Gaussian profiles with the same peak intensity as the respective profile, but with linewidths of FWHM = 2 km s^{1} and FWHM = 4 km s^{1}, matching the observed total velocity dispersions. For each transition the yoffset corresponds to the position with the highest lineintegrated intensity. 

In the text 
Fig. A.1
Large map (see text) showing one representation of the test ensemble, consisting of randomly distributed clumps. The colour scale gives the line centre intensity of the 1.9 THz [Cii] transition where the line intensity is highest in the outer layer of each clump. This maps shows the 22nd representation from Fig. A.3. The ensemble is shown in a superpixel, in other words with the area filling factor of a normal 0.01 pc pixel, but scaled in the lateral directions to contain enough clumps of each size. 

In the text 
Fig. A.2
Same as Fig. A.1 but with the colour scale giving the optical depth of the 1.9 THz [Cii] transition. 

In the text 
Fig. A.3
Ensembleaveraged [Cii] line centre intensities for different realisations of the test ensemble. The circles show the ensembleaveraged intensities of 20 different small intensity maps and the squares give the values for ten different large intensity maps as presented in Fig. A.1. The black line gives the mean value of the 30 maps, weighted by the respective number of grid points. For the 20 small maps the average is (2.24 ± 0.50) K and for the ten large maps it is (2.17 ± 0.33) K. The stated error is the standard deviation, which, as expected, is larger for the smaller maps. The red, dashed line gives the value derived using the probabilistic approach for the same ensemble. The difference between the two approaches is about 2.6%. 

In the text 
Fig. A.4
Same as Fig. A.3 but plotted for the ensembleaveraged optical depths, τ, of the [Cii] line. For the [Cii] line the difference between the probabilistic approach and the average over the 30 results derived with the direct approach is about 2.6% for line intensities and optical depths. 

In the text 
Fig. A.5
Small map of one representation of the test ensemble. The colour scale gives the optical depth of the CO 3−2 line (line centre), which is highest for lines of sight through the dense cloud cores. This map shows the first realisation from Fig. A.6. The increasing optical depth towards the clump centres provide the visual impression that the clumps are smaller than their actual size. 

In the text 
Fig. A.6
Ensembleaveraged optical depths of the CO 3−2 line. The circles show the results for ten different realisations of the ensemble as presented in Fig. A.5. The black line gives the mean value of the ten maps, namely (0.038 ± 0.006) where 0.006 is the standard deviation. The red, dashed line gives the value derived with the probabilistic approach for the same ensemble. The difference between the two approaches is about 3.7% (and about 3.5% for the line intensities which are not shown). 

In the text 
Fig. A.7
Small map showing the line centre intensity of the CO 16–15 line (colour scale). This map corresponds to the 5th representation from Fig. A.8 where the large clump is sitting at the lower edge, causing a small ensembleaveraged intensity and optical depth. 

In the text 
Fig. A.8
Ensembleaveraged intensities of the CO 16–15 line (line centre). The circles show the results for ten different realisations of the ensemble as presented in Fig. A.7. The black line gives the mean value of the ten maps, namely (7.6 ± 1.0) K where 1.0 is the standard deviation. The red, dashed line gives the value derived by the probabilistic approach for the same ensemble. Here, the two lines are lying on top of each other (the difference between the two approaches is about 0.004% for the line intensities and about 0.8% for the optical depths which are not shown here). 

In the text 
Fig. A.9
Intensity map showing a representation of the interclump medium with initially 100 identical clumps (a few clumps have been cut away during the removal of the edges of the map). The colour scale gives the line centre intensities of the [Cii] line. 

In the text 
Fig. A.10
Ensembleaveraged [Cii] line centre intensities of different realisations of the interclumpensemble. The circles show the ensembleaveraged intensities of ten different maps containing ten clumps and the squares give the values for ten different maps with 100 clumps as presented in Fig. A.9. The black line gives the mean value of the 20 maps, weighted by the respective number of grid points. For the maps with ten clumps the average is (24.4 ± 7.1) K and for the maps with 100 clumps it is (23.6 ± 1.0) K, where the stated error is the standard deviation. The red, dashed line gives the value derived by the probabilistic approach (for N_{nM = 1} = 100 clumps) for the same ensemble. The two results agree within 0.4% (intensities) and 0.8% (optical depths, not shown). 

In the text 
Fig. A.11
Map showing the interclump medium of model 6j, with a surface density corresponding to the full line of sight of the model provided by 80 voxels. The colour scale shows 1−Exp(−τ_{CII}). 

In the text 
Fig. D.1
Simulated cuts perpendicular to the Orion Bar, based on model Cyl. 5 (see Table D.1). Each colour scales gives the lineintegrated intensity of the transitions indicated above the respective cut. 

In the text 
Fig. D.2
Cut through the cylindrical Orion Bar model Cyl. 4. For each voxel the colour scale gives the [Cii] line intensity of dense clumps and interclump medium, at the line centre (at 11.3 km s^{1}). The illuminating star Θ^{1} Ori C is located at [0,22.3,30]. 

In the text 
Fig. D.3
Same as Fig. D.2 but plotted for the outer line wing (at 8.3 km s^{1}). Effectively, only the interclump medium contributes at this velocity. 

In the text 
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