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J. R. Goicoechea et al.: Low sulfur depletion in the Horsehead PDR, Online Material p 3Fig. A.2. Thermalization tests for a plane-parallel cloud illuminated by the cosmic background at both surfaces (z/z max = 0,1).In order to benchmark the spatial distribution of the PDR codeabundance predictions with our interferometric line observations,we now can use the simple model described above to computethe synthetic spectrum of a required molecule. To do that,the PDR code output was used as an input for the RT calculation.In particular, the density profile (both n(H 2 )andn(H)), temperatureprofile (both T k and T d ) and species abundance are carefullyinterpolated from the PDR spatial grid output. In practice,the slab discretization for the RT calculation has to be preciseenough to sample the abundance, density and temperature variatel-00726959,version 1 - 31 Aug 2012from different spatial regions contribute to J¯ij . Hence, the solutionof molecular excitation implicitly depends on the nonlocalradiation field, which obviously depends on level populationsin many cloud points. J¯ij is explicitly computed in theMonte Carlo approach, and thus, the RT-excitation problem issolved iteratively until desired convergence in some physical parameter(generally the level populations) is achieved. LTE levelpopulations at a constant fictitious T ex were used for the first iteration.In the case of CS modeling, T rot from the observationalrotational-diagrams (Fig. 5) was used.The RT problem is then simulated by the emission of a determinednumber of model photons (both sides external illumination,continuum and line photons) in a similar way to thatoriginally described by Bernes (1979). Model photons representa large quantity of real photons randomly distributed over theline profile A.5 and emitted at random cloud positions and directions.Each model photon is followed through the differentslabs until it escapes the cloud or until the number of representedreal photons become insignificant. Note that the angle θ betweenthe photon direction and the normal to the slabs remains constantin all the photon path. In spherical geometry the θ anglebetween the photon direction and the radial direction changesin each photon step and thus has to be computed repeatedly.In addition, model photons sent in the cos θ ≃ 0 direction insemi-infinite plane-parallel geometry will almost never escapethe cloud. Thus, a minimum number of represented real photonsis defined otherwise the photon is not followed anymore.In this way, the Monte Carlo simulation explicitly computes theinduced emissions caused by the different types of model photonsin all the slabs. At the end of the simulation, an averagedvalue for the B ij J¯ij that independently accounts for external illumination,continuum emission and line emission is stored forevery slab ( ∑ S ij,m in Bernes formalism). Populations are thenobtained in each slab by solving:∑ [ ∑ ] ∑n i Aij + S ij,m + C ij =jijin j[gig j∑S ij,m + C ji]. (A.9)A reference field for all types of model photons was included toreduce the inherent random fluctuations (i.e. the variance) of anyMonte Carlo simulation (see Bernes 1979, for details). Whena prescribed convergence in level the populations is reached, thetotal (gas+dust) source function is completely determined andthe emergent intensity can be easily computed by integratingEq. (A.2).For spherical geometry, the code was successfully benchmarkedagainst two test problems, the Bernes’ CO cloud (Bernes1979) and the HCO + collapsing cloud, problem 2a of the 1999Leiden benchmark (van Zadelhoff et al. 2002). In the caseof plane-parallel geometry, several thermalization tests for theldepthδxinclination angle ϕinterferometerFig. A.3. Adopted geometry for a plane-parallel PDR inclined byan small angle ϕ relative to the line of sight s. In this sketch, z denotesthe normal direction to the slabs and also the UV illumination direction.CS excitation (without dust emission) were successfully performed.Excitation temperatures as a function of the normal coordinateto slabs z are shown for CS J = 1–0, 2–1, 3–2 and5–4 transitions in Fig. A.2. Model parameters are T k = 20 K,n(H 2 ) = 10 5 cm −3 and χ(CS) = 7 × 10 −9 .Different excitationconditions are considered. Upper model: A ij /C ij = 0andT exis correctly thermalized to T kin (LTE). Middle model: collisionalrates from Lique et al. (filled squares; 2006) and Turneret al. (empty circles; 1992) and resulting non-LTE excitation(see Sect. 5.1). As noted by Lique et al., their new collisionalrates produce larger excitation temperatures, especially as J increases.For the Horsehead physical conditions this implies thatthe estimated densities and/or abundances with Turner et al. collisionalrates are ∼10% larger. Lower model: collisional excitationneglected and T ex is radiatively thermalized to the cosmicbackground temperature at T bg = 2.7 K.A.2. A model for an edge-on cloud with inclinationϕUV illuminationsz
J. R. Goicoechea et al.: Low sulfur depletion in the Horsehead PDR, Online Material p 4tions at the edge of the PDR (where most of the changes occur).In most RT computations, ∼50 slabs were judged to give satisfactorysampling of the PDR variations. For an edge-on configuration,after a Monte Carlo simulation, RT Eq. (A.2) wasintegrated in a grid of different lines of sight (similar to impactparameters in spherical geometry) as depicted in Fig. A.3.Lines of sight can be inclined by an angle ϕ respect to the slabsnormal (ds = dz/ sin ϕ). Therefore, the maximum integrationpath is l depth / cos ϕ where l depth is the assumed cloud spatialdepth. To produce a synthetic map, results are then convolvedin a grid of cloud points with an angular resolution characterizedby a Gaussian with hpbw equal to that of the synthesizedinterferometric beam.tel-00726959, version 1 - 31 Aug 2012
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Table des matières1 Rapport de sou
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Rapport après soutenanceHabilitati
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Chapitre 2Curriculum vitaetel-00726
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2.7 ANIMATION ET DIFFUSION DE LA CU
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2.8 PARCOURS 131992-1993 ÉCOLE NOR
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Chapitre 3Copyright: IRAM/PdBIIntro
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4.2 ETUDES DIRECTES EN ÉMISSION 19
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4.4 LA LUMINOSITY CO PAR MOLÉCULE
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A&A 518, A45 (2010)Table E.2. Data
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CLASS evolution: I. Improved OTF su
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S. Maret et al.: Weeds: a CLASS ext
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8.3 PERSPECTIVES 227tel-00726959, v
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3 REQUIREMENTS 44 CHANGES FOR END-U
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5 CHANGES FOR PROGRAMMERS 125 CHANG
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A EXHAUSTIVE DESCRIPTION OF THE CHA
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