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Modelling multiwavelength SEDs – tools for galaxy formation models

Modelling multiwavelength SEDs – tools for galaxy formation models. Laura Silva - INAF Trieste. Gian Luigi Granato, Andrew Schurer (INAF); Cedric Lacey, Cesario Almeida, Carlton Baugh, Carlos Frenk (ICC); Olga Vega (INAOE); Fabio Fontanot, Alessandro Bressan (INAF), Pasquale Panuzzo (CEA).

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Modelling multiwavelength SEDs – tools for galaxy formation models

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  1. Modelling multiwavelength SEDs – tools for galaxy formation models Laura Silva - INAF Trieste Gian Luigi Granato, Andrew Schurer (INAF); Cedric Lacey, Cesario Almeida, Carlton Baugh, Carlos Frenk (ICC); Olga Vega (INAOE); Fabio Fontanot, Alessandro Bressan (INAF), Pasquale Panuzzo (CEA) • Plan: • * Modelling SEDs • GRASIL- characteristics, aims and limitations • Fitting observed SEDs • Effects of different SED treatmens • * Application to SAMs: GALFORM, ABC, MORGANA +GRASIL • * Modelling SEDs with Artificial Neural Networks • * SEDs for SPH: GRASIL3D

  2. Multi- SED modelling – ingredients & aims *SFR(t)+Mgas(t),Z(t) analytical, chemical evolution or galaxy formation models *Stellar pop. synthesis * UV/optical attenuation and IR emission • Semi-empirical: attenuation curve for LIR + IR shape. Pros: non time consuming – analysis of large data sets. Cons: not great predictive power Theoretical:Explicitcomputation of radiative transfer anddustemission Pros: broader interpretative/predictive power. Cons: timeconsuming

  3. Modelling UV to radio SEDs with GRA(phite)SIL(icate) 1) Realistic and flexible SED modelling • Stars and dust in a bulge (King profile) + disk (double exponential) • 3 dusty environments: dense (star forming Molecular Clouds), diffuse(cirrus) ( clumping of stars and dust), dusty envelopes of AGB stars • Stars are born within MCs and gradually escape as a function of their age  age-dependent extinction Star- forming MCs • Dust: big grains, very small grains and PAHs. Emission is appropriately computed for each component Extincted stars • UV-to radio SEDs Diffuse dust 2) Reasonable computing time • Presence of symmetries • Radiative transfer exactly solved for opt thick MCs, with approximation in the cirrus (real bottle-neck)

  4. UV Attenuation in spiral galaxies – role of age-dependent extinction (Panuzzo+2007) MW Sample of GALEX NUV-selected late type galaxies (Buat+, Iglesias-Paramo+) SMC Meurer et al 1999 UV-bright sb • No age-dependent extinction • Sequence of models with increasing dust content (1m polar opt depth =0.05-6.4) • MW and SMC dust composition • 2175A bump within NUV • Best fitting models: • Age-dependent extinction due to star forming Molecular Clouds and Cirrus (stellar age stratification in the disk wrt dust) • Sequence of models with increasing dust content in the cirrus and age for thin-thick disk separation tthin=25-200 Myr

  5. SED analysis of ULIRGs (Vega+2008) MC Sample of 30 nearby ULIRGs w MIR to radio data vs large grid of SF+GRASIL+AGN tori models AGN Sync cirrus ~70% SB dominated (Lagn/Lbol<10%) ff •  SBs • SB+AGN •  AGN subtracted ESB>4 Post-sb ESB=2-4 evolved ESB=Age/e-folding time ESB=0.2-2 peak phase ESB<0.2 early phase

  6. *Tot o Agn subtracted LIR/Mden for SB= 180 Mo/Lo=> M/LHCN=5 Gao & Solomon 04 LIR/Mden=90 Mo/Lo with M/LHCN=10 Mo/(K km/s pc2)

  7. Different treatments predict different SED for the same SFR(t): Attenuation SAM + GRASIL SAM + [C&F00 + slab ] SF histories from the Semi-Analytical Model for galaxy formation MORGANA - SED by GRASIL (colored) and empirical [attenuation curve with C&F + slab] (hatched) Fontanot, Somerville, Silva+09

  8. SAM + GRASIL ||||||||||| SAM+[C&F00 + slab] Fontanot, Somerville, Silva+09

  9. Different treatments predict different SED for the same SFR(t): IR MORGANA+ template MORGANA+ GRASIL (average SEDs for low-z and high-z mock catalogues) Fontanot, Somerville, Silva+09

  10. Different treatments predict different SED for the same SFR(t): IR SAM+GRASIL SAM+ templates [Chary&Elbaz01, Lagache+04, Devriendt+99] SF histories from the Semi-Analytical Model for galaxy formation MORGANA - SED by GRASIL (black) and templates (colored) Fontanot, Somerville, Silva+09

  11. Effects of dust assumptions on SED (Schurer+09) Representative SF for Spirals (MW-type), Ellipticals and Irr + evolution of C- and Si- based dust with assumptions on dust production (evolved stars, SN ejecta) and distruction efficiencies constrained by chemical abundances & dust depletion (Calura, Pipino & Matteucci 08) Mdust/Mgas(t)  Z Z from chem. model MW-type M*=10^12 Z Ellipticals M*=10^11 M*=10^10 Irregular Z

  12. model + MW ext Young Elliptical model vs Balmer-break galaxies (Wiklind et al 2008) model + QSO ext Mdust Z + MW ext MW ext curve QSO ext curve (Maiolino+04)

  13. Young elliptical model vs SHADES sources (Clements et al 2008) MW ext curve QSO ext curve (Maiolino+04) model + MW ext model + QSO ext Mdust Z + MW ext

  14. Computing SEDs in Semi-Analytical galaxy formation Models • SAM: DM with gravity-only N-body or MC, baryons with analytical recipes – compare with widest range of observed galaxy properties • Outputs: simulated catalogues of galaxies at different z slices; SFR(t), Mgas(t), Z(t), morphology, scale radii for stars & gas • Associate to each mock galaxy its “real” SED but:complexities in treating radiative effects - unknown dust properties - computing time fundamental issue for cosmological volumes • Semi-empirical treatment: fix v (L or f(Mgas, Z)) +  dependence + uniform distrib. of stars and dust in a 1D slab + IR templates • SAMs with theoretical SED:GALFORM+GRASIL(Granato+00,Silva+01,Baugh+05,Lacey+08,09) Anti-hier.BarionicCollapse+GRASIL(Granato+04,Silva+05,Lapi06) • MORGANA+GRASIL(Monaco+07,Fontanot+07,09)

  15. GALFORM(Cole+00)+GRASIL Granato+00; Baugh+05; Lacey+08,09 Local universe

  16. Revised model: reproduce multi- LFs and counts/z-distr w top-heavy IMF in starbursts But high-z universe 850m Old model 850m New model

  17. Anti-hierarchicalBaryonicCollapse (ABC) + GRASIL Granato+01,04,06; Silva+05; Lapi+06 Aim: get downsizing within hierarchical assembly of DM to explain high-z massive galaxies & ell with SAM: *cooling gas in big halos at high-z start vigorous SF without setting in a disk *SFR promotes the development of SMBH from a seed, feedback of the QSO on the host to possibly quench SF MBH(t)/1e5 SFR ṀBH(t)*300

  18. SCUBA 850 m data model MAMBO 1200 m

  19. K Band counts and z distribution K20 SURVEY mass range required by sub-mm counts All sph Passive sph model Extremely Red Objects (R-K)>5 observed passive active

  20. Modelling SEDs with Artificial Neural Networks Almeida+09, Silva+09 • Aim: computing SEDs with GRASIL but much faster (now: several minutes)  Exploit the Millennium Simulation – a mock galaxy catalogue requires millions runs • Improve on RT approximations • Fast search for best fit parameters for large data sets L IR > 10^11 Lo L IR > 10^12 Lo S(100m)>2mJy Lacey+09

  21. Spectral variance for a GALFORM + GRASIL catalogue Why ANN: • SEDs : complex, non-linear, high dimensionality and large variance functions of some galaxy properties * Mathematical algorithms for data analysis, introduced to replicate the brain behavior: learn from examples* It works!

  22. ANN algorithm (black box) Input: parameters determining the SED Output: SED The ANN is trained to predict the SED from controlling parametersusing a suitable precomputed training set (many sets of known input-output)

  23. ANN & SED: 2 methods General use - very fast(Silva+09): input = physical quantities determining the SED of MCs and Cirrus – one single trained net for any application Specific for GALFORM+GRASIL - extremely fast(Almeida+09): input = galaxy properties – re-train the net for different realizations M*, Z*, Zgas, Lbol, vcirc for disc & bulge, R1/2 for disc & bulge, Tgal, V, M*burst, tlast burst Each simulated catalogue requires a trained net << 1 sec -> potentially exploit the whole Millennium Simulation • MCs: Optical depth , R/Rmin • Cirrus: Ldust, Mdust, Polar & Equatorial opt depth, R*/Rdust, z*/R*, zdust/Rdust, Hardness of the rad. field • “ANN mode” implemented in GRASIL: compute extinction and predict IR SEDs with separately trained ANN for MCs and Cirrus • ~ 1 sec -> large cosmological volumes

  24. ANN GALFORM+GRASIL Almeida+09 Quiescent Bursts log Lann/Lorig vs log Lorig  rest=0.17m z=3 catalogue  rest=24m z=0.5 catalogue  obs=850m z=2 catalogue

  25. z=0.5 =24m z=3 =0.17m z=2 =850m Bursts Bursts Bursts Quiescent Quiescent Quiescent

  26. ANN GRASIL Silva+09 Input neurons for star forming Molecular Clouds MCMMC/RMC2 RMC /Rmin2 2 RMC/L*,MC RMC /Rmin

  27. Input neurons for Cirrus Ldust/L*: ~amount of dust reprocessing Mdust/L*: ~overall distrib of dust Tdust:polar, equatorial, homog - ~ measure concentration of dust R*/Rdust: ~relative position of * and dust Hardness of radiation field: ~ MIR to FIR ratio Hardness1.7 Mdust2 /10

  28. ANN vs GRASIL - Examples of singleSEDs M51 M82

  29. ANN vs GRASIL with ABC SAM–randomlyextractedSEDs

  30. ANN vs GRASIL - ABC mockgalaxiesmakingsubmmcounts ANN vs GRASIL – GALFORM z=0catalogue

  31. ANN vs GRASIL for ABC – comparisonforgalaxycounts 100m 70m 160m 250m 350m 500m

  32. SED and SPH galaxymodels: GRASIL3D A.Schurer 09 PhDtheses • Aim: exploit the spatial information for stars and gas in hydro simulations of galaxy formation and of observed images – requires no symmetries • GRASIL->3D: • generalised to an arbitrary geometry through a cube grid in which stars and gas particles output by the SPH are distributed • Gas in each cell divided in SF molecular clouds and cirrus (if young stars are present and gas density > threshold) • Intrinsic stellar SED in each cell, with young stars within MCs • Radiation field in each cell due to all other cells1° Application : P-DEVA (Serna & Dominguez-Tenreiro) + GRASIL3D

  33. z = 3.5 z = 2 z = 0 t/to red – STARS yellow - GAS

  34. z = 3.5 z = 2 z = 0

  35. Images: face on at z=0

  36. Images: Edge on at z=0

  37. Preliminary tests: z = 2, comparison to SCUBA galaxies

  38. SUMMARY • Multi-wavelength modelling as a tool to quantitatively interpret observations – make predictions and constrain galaxy formation models • Different treatments predict different SEDs for the same SFR(t)-> necessity of a reliable computation of the SED for proper interpretations of observations and predictions of galaxy formation models • The treatment of dust reprocessing of UV/optical in the IR requires a proper computation – time cosuming • For large cosmological applications: promising solution with ANN

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