Dust radiative transfer modelling. Maarten Baes

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1 Dust radiative transfer modelling Maarten Baes

2 Outline 1. Dust radiative transfer: what and why? 2. 3D radiative transfer: how? 3. Simple mock galaxies 4. More realistic mock galaxies 5. Inverse radiative transfer (fitting models to data) 6. Summary

$Interstellar dust Cosmic dust: a minor fraction of the ISM in galaxies But a crucial constituent: regulates$ crucial for planet formation organic material Important aspect for galaxy modellers: dust absorbs 30-50% of

crucial for planet formation organic material Important aspect for galaxy modellers: dust absorbs 30-50% of

3 Interstellar dust Cosmic dust: a minor fraction of the ISM in galaxies But a crucial constituent: regulates the physical and chemical conditions of the interstellar gas catalyzes star formation reservoir of metals crucial for planet formation organic material Important aspect for galaxy modellers: dust absorbs 30-50% of all the starlight in the Universe and converts it to infrared radiation. Fritz et al. 2012; Smith et al. 2012

It is a very general problem with applications in all sciences Generally described by the

4 The radiative transfer problem The radiative transfer problem describes the interaction between radiation and matter. It is a very general problem with applications in all sciences Generally described by the following equation (RTE) Rate of change of the intensity of the radiation field Sinks: removal of radiation due to interaction with matter Source terms: creation of radiation

5 The dust radiative transfer problem In the case of interstellar dust, the RT becomes a bit more complex Difficult? partial integro- differential equation in six dimensions dust emissivity is a complex nonlinear function of the mean intensity nonlocal (in all dimensions) geometry often complicated (3D, inhomogeneous ) optical properties of the dust are relatively poorly known

6 Can we make life simpler? Interesting options to simplify the RT problem: plainly ignore dust neglect scattering (only absorption) approximate scattering use a simple geometry like a screen geometry Shown by many authors since the late 1980s that these simplifications are never satisfactory. Witt et al. 1992: much previous work on the effects of dust on stellar radiation has been simplistic, wrong, and usually both! Disney et al. 1989; Witt et al. 1992; Byun et al. 1994; Baes & Dejonghe 2001;

7 but no panic! After a few decades in the dark, the future is bright for radiative transfer There has been enormous progress on different areas interesting observational data available! increase in computing power (storage, speed and memory) improvement in algorithms: many techniques have now become standard/mature many codes now contain the necessary ingredients to do realistic simulations absorption, scattering polarisation (some) LTE and NLTE dust emission (big grains, VSGs, PAHs) arbitrary (and complex) 3D geometries data modeling (inverse RT) has become available

8 Which method to use There are different approaches to solve the dust radiative transfer problem which one should one choose? Finite difference approach uses the same techniques as employed for hydrodynamics results in a complex system of linear equations in practice: only used for 1D (and 2D?) dust radiative transfer popular in a simplified form (e.g. flux- limited diffusion approximation) to couple to hydrodynamics as RHD Moment method expands the solution in terms of spherical harmonics fast and nice for 1D, possible in 2D, still to be applied to 3D can exhibit non- physical oscillations

9 Which method to use Ray- tracing calculates the intensity by integrating the RTE along a number of fixed rays through the domain explicit error control is possible treatment of multiple anisotropic scattering is a challenge also popular in simplified form for RHD Monte Carlo technique stochastic simulation technique could be considered as a special case of ray- tracing, in which the choice of the rays is determined by random numbers intrinsically 3D easy to add additional physics Poisson noise is unavoidable

Monte Carlo radiative transfer probabilistic simulation method (does not solve the radiative transfer equation) A large number of photon packages are followed individually through the dusty medium.

12 Monte Carlo radiative transfer probabilistic simulation method (does not solve the radiative transfer equation) A large number of photon packages are followed individually through the dusty medium. The trajectory of each photon package is determined by (pseudo) random numbers. The radiation field is reconstructed by classifying the photon packages by position, propagation direction, wavelength For details, see Witt 1977; Bianchi et al. 1996; Gordon et al. 2001; Juvela 2005; Whitney 2011; Steinacker et al. 2013; and many more

13 MCRT optimization techniques The simple Monte Carlo cycle is horribly inefficient for general 3D geometries. Solution: optimization tricks to increase the efficiency. biasing (pick a wrong probability function and correct for it) avoid the need to generate random events by including deterministic elements Steinacker et al. 2013

14 MCRT optimization techniques Some examples of popular optimization techniques for dust MCRT biased emission: preferentially shoot photons to interesting directions forced scattering: don t waste photons that leave the system without interacting peel- off technique: drastically increase the signal- to noise in observed images/seds continuous absorption: absorb along the ray instead of at one single location polychromatism: shoot photon packages that contain photons at many different wavelengths Juvela 2005

15 Baes et al. 2003, 2011; Camps & Baes

16 Benchmark of several 3D dust RT codes on a suite of well- defined problems Very preliminary results suggest that we can reach agreement at the ~percent level. Gordon et al., in prep.

17 Benchmark of several 3D dust RT codes on a suite of well- defined problems Very preliminary results suggest that we can reach agreement at the ~percent level. Gordon et al., in prep.

18 Byun et al Simple mock galaxies

19 Simple mock galaxies Bulge luminosity Optical depth

20 Simple mock galaxies Bulge luminosity Optical depth

21 Simple mock galaxies Optical depth Bulge luminosity

22 Galaxy observables Möllenhoff et al. 2006

23 Bulge- disc decomposition Gadotti et al. 2010

24 Bulge- disc decomposition Pastrav et al. 2013

25 The effects of clumpiness Indebetouw et al. 2006

26 Simple mock galaxies: bottom- line often computationally reasonable (due to symmetries) full treatment of absorption and scattering is crucial no simplifications can do the job dust can severely affect structural parameters (flux, surface brightness, effective radius ) same for the derived values from B/D decompositions star- dust geometry is crucial special case: clumpiness. The attenuation cannot be simply averaged out.

In particular: mock galaxies from hydro simulations.

27 Realistic mock galaxies Star/dust geometry is important: we should apply RT on more realistic galaxy models. In particular: mock galaxies from hydro simulations. Great: they have become more and more realistic in the past few years. Problem: large dynamic range and lack of symmetry Guedes et al Marinacci et al. 2014

k- d trees (binary trees) are 50% more efficient and equally simple to implement.

28 Advanced grids in MCRT (1) Hierarchical grids octree grids k- d tree grids (more efficient!) more general: AMR grids Octrees are now implemented in quite a number of modern MCRT codes. k- d trees (binary trees) are 50% more efficient and equally simple to implement. Shooting photon packages through these grids should be done in a clever way! Modern MCRT codes can now run simulations with several million grid cells Saftly et al. 2013, 2014 Kurosawa & Hiller 2001

photon packages through a Voronoi grid is

29 Advanced grids in MCRT (2) Unstructured Voronoi grids best local resolution for a given number of cells ideal for coupling to moving- mesh hydro codes Shooting photon packages through a Voronoi grid is more efficient as one would think Camps et al. 2013

30 Jonsson 2006 Post- processing hydro simulations

31 Post- processing hydro simulations Natale et al. 2015

32 Post- processing Eris Camps Saftly et et al., al. in 2015 prep

33 The EAGLE simulation suite of N- Body/SPH simulations full cosmological context (ΛCDM) box size from 25 to 100 Mpc up to 3 billion particles mass resolution ~105 to 106 Msun Schaye et al. 2015

34 Post- processing an EAGLE galaxy Camps et al., in prep

35 The EAGLE simulation Schaye et al. 2014

36 EAGLE color- magnitude diagram Analytical correction, standard EAGLE simulations Trayford et al., in prep

37 EAGLE color- magnitude diagram Analytical correction, different resolution simulations Trayford et al., in prep

38 EAGLE color- magnitude diagram Full dust RT simulations Trayford et al., in prep

39 Challenges (1) For realistic dust RT models, we need to resolve the large- and small- scale structure of the dusty ISM in galaxies. Simulated mock galaxies often under- resolved Sub- grid recipes, templates MCRT acceleration techniques Steinacker et al High- resolution simulated galaxies are complex. 3D dust RT simulations computationally expensive. Advanced grids (hierarchical and unstructured grids) Saftly+ 2013, 2014; Camps Specialized parallelization strategies for MCRT Verstocken et al. in prep

40 Challenges (2) Simulated mock galaxies predict the distribution of stars and gas (and DM), but not for the dust. Use a proxy/recipe for the dust (total gas, molecular gas, metallicity ) Include dust formation and evolution into hydrodynamical simulations Bekki 2015; McKinnon et al McKinnon et al. 2015

41 Inverse radiative transfer Model a particular real galaxy by fitting radiative transfer models to observational data (SED, images, ). Model this galaxy!

42 Inverse radiative transfer Model a particular real galaxy by fitting radiative transfer models to observational data (SED, images, ). Major complications Star- dust geometry: large number of parameters to be fitted o Intrinsic geometry o Orientation o Dust properties Run- time for a single simulation (i.e. a single point in the multi- dimensional parameter space) is considerable Memory requirements as well Monte Carlo noise makes fitting more complicated

43 Fitting algorithms Poisson noise on Monte Carlo simulations prevents the standard fitting routines (Levenberg- Marquardt)

44 Steinacker et al. 2005

and surface brightness profiles fairly well. Xilouris et al.

45 Monochromatic models Radiative transfer fits, including absorption and scattering, manage to reproduce optical images and surface brightness profiles fairly well. Xilouris et al see also Kylafis & Bahcall 1987; Bianchi 2007; Matthews & Wood 2001

46 Monochromatic models Radiative transfer fits, including absorption and scattering, manage to reproduce optical images and surface brightness profiles fairly well. B mag/arcsec z offset (kpc) V mag/arcsec z offset (kpc) I mag/arcsec Dust distribution 22.5 in spiral galaxies is geometrically 23.5 thin, radially extended and moderately z offset (kpc) opaque (τ V 0.8). Xilouris et al see also Kylafis & Bahcall 1987; Bianchi 2007; Matthews & Wood 2001

47 Schechtman- Rook et al. 2012

48 V- band image Model this galaxy! V- band model Yes, sir Baes et al De Looze et al. 2012a

49 FitSKIRT Tool to fit parametric 3D models to a set of UV/optical/NIR images. couples the SKIRT radiative transfer code to a genetic algorithm optimization routine (almost) fully automated and unbiased De Geyter et al. 2013

50 De Geyter et al. 2014

Models agree largely with previous results for individual galaxies large spread

51 g r i z g r i z observed FitSKIRT fit Sample of 12 edge- on spiral galaxies from CALIFA survey: 10 could be accurately reproduced. Models agree largely with previous results for individual galaxies large spread within sample for face- on optical depth dust disks about 70% more extended than stellar disks De Geyter et al. 2014

52 Polychromatic models 1. repeat the same trick for different wavelengths 2. use the stellar distribution of the resulting model at each wavelength to calculate the ISRF at each position 3. use these ISRF to calculate the FIR/submm emission 4. (iterate to take into account dust self- absorption) Dust energy balance problem: dust seen in absorption underestimates dust seen in emission See also: Popescu et al. 2011; Bianchi 2008; Baes et al. 2010; De Looze et al. 2012a,b; a

53 Mocking the infrared Universe

54 Dust energy balance problem NGC 5166: radiative transfer model predicts the SED (and the images from UV to submm) fairy well IC 4225: dust energy balance problem: FIR fluxes underestimated by a factor of about three! De Geyter et al. 2015

55 RT modeling of face- on galaxies Advantages/disadvantages attenuation hard to quantify assumptions need to be made about the vertical structure small- and large- scale structure can be taken into account many (large) galaxies with excellent UV- submm data sets available ~100 large galaxies with D 25 > 5 arcmin multi- wavelength imaging (including Herschel)

56 GALEX FUV young stars Optical IRAC 3.6 µm evolved stars IRAC 8 µm hot dust PACS 160 µm cool dust

57 RT model of M51 Dust emission from the model agrees with the observed FIR SED. No dust energy balance problem. Global dust heating is dominated by young stellar populations, but old stars do contribute substantially as well (37%). De Looze et al. 2014

RT model of M31 10 11 10 10 Direct Scatter Dust

10 1 10 0 10 1 10 2 10 3 /µm Heating by evolved stars

58 RT model of M Direct Scatter Dust Transparent Total Observed Luminosity/L /µm Heating by evolved stars Heating by young stars Viaene et al Viaene et al., in prep

59 Summary 1. Simple recipes don t work 3D dust radiative transfer is the only option. 2. 3D radiative transfer can do the job computing power and algorithms are available advanced grids and parallelization schemes are being worked on 3D benchmarks are ongoing (and promising) fitting individual galaxy images now possible 1. There are still significant challenges constant struggle to make codes more efficient small and large- scale star/dust geometry is crucial (and we don t always have that information available) fitting models to data remains a very hard challenge we still need to know more about the dust

Andromeda in all colours

Andromeda in all colours Sébastien Viaene and the HELGA team: J. Fritz, M. Baes, G.J. Bendo, J.A.D.L. Blommaert, M. Boquien, A. Boselli, L. Ciesla, L. Cortese, I. De Looze, W.K. Gear, G. Gentile, T.M.