1. Modelling multiwavelength SEDs – tools for galaxy formation models 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 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)

2. Young massive stars –> hot dust (peak ~60  m) in star forming regions NTh radio allow finer age tuning for starbursts – SNRate depends on recent SFR AGB dusty envelopes – silicate emission Hot optically thick thermal equilibrium dust – silicate absortion – PAH bands Older stars –> cold (peak ~100  m) diffuse dust (cirrus) Hot optically thin non thermal equil. dust - PAH bands High mass X-ray bin. Low mass X-ray bin. SED modelling: tool to quantitatively interpret observations & constrain galaxy formation models

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

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

5. 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 t thin =25-200 Myr Sample of GALEX NUV-selected late type galaxies (Buat+, Iglesias-Paramo+) MW SMC 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 UV Attenuation in spiral galaxies – role of age-dependent extinction (Panuzzo+2007) Meurer et al 1999 UV-bright sb

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

7. * Tot o Agn subtracted L IR /M den for SB = 180 Mo/Lo => M/L HCN =5 Gao & Solomon 04 L IR /M den =90 Mo/Lo with M/L HCN =10 Mo/(K km/s pc2)

8. AGN dominated Best fit without or with IRS spectrum

9. 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 Different treatments predict different SED for the same SFR(t): Attenuation

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

11. MORGANA+GRASIL low-z catalogue MORGANA+GRASIL high-z catalogue MW ext Calzetti00 C&F00 MW ext + slab

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

13. Fontanot, Somerville, Silva+09 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) Different treatments predict different SED for the same SFR(t): IR

14. Effects of dust assumptions on SED (Schurer+09) MW-type Ellipticals Irregular  Z Z from chem. model M dust /M gas(t ) M*=10 ^12 M*=10 ^11 M*=10 ^10 ZZ ZZ ZZ 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)

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

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

17. 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 Associate to each mock galaxy its “real” SED but: complexities in treating radiative effects - unknown dust properties - computing time fundamental issue for cosmological volumes 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) Outputs: simulated catalogues of galaxies at different z slices; SFR(t), Mgas(t), Z(t), morphology, scale radii for stars & gas  Semi-empirical treatment: fix  v (  L or  f(Mgas, Z)) + dependence + uniform distrib. of stars and dust in a 1D slab + IR templates

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

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

20. SFR M BH (t)/ 1e5 Ṁ BH (t) *300 Anti-hierarchical Baryonic Collapse (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

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

23. SMBH growth in SMGs dM/dt(BH)>0.013 M/yr L(0.5-8kev)>1E43 erg/s dM/dt(BH)>0.13 M/yr L(0.5-8kev)>1E44 erg/s (Granato et al 2006)‏ 20 − 50% of bright ( > 5 mJy) SCUBA sources hosts mild AGN activity, with X ray (0.5- 8 keV) intrinsic luminosity between 1E43 and 1E44 erg s −1 (Alexander et al)

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

25. 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 Lacey+09 L IR > 10^11 Lo L IR > 10^12 Lo S(100  m)>2mJy

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

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

28. Input layer: parameters determining the SED Output layer: SED, one unit for each L( ) Hidden layers w jk n j =  w jk i k o j =f(n j ) Propagation rule: the output from each unit is weighted and summed to form the input for the upper layer units: n j =  w jk i k The new output is o j =f(n j ), f=non linear function Learning: the ANN is trained with a given target- weights are adjusted to best approximate a given set of inputs/outputs

29. 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 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 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, R 1/2 for disc & bulge, Tgal,  V, M*burst,  t last burst Each simulated catalogue requires a trained net potentially exploit the whole Millennium Simulation

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

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

32. ANN GRASIL Silva+09 Input neurons for star forming Molecular Clouds  2 R MC /R min  2 R MC /  L *,MC  R MC /R min  MC   M MC /R MC 2

33. Input neurons for Cirrus Hardness  1.7 Mdust  2  /10 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

34. ANN vs GRASIL - Examples of single SEDs M51 M82

35. ANN vs GRASIL with ABC SAM– randomly extracted SEDs

36. ANN vs GRASIL - ABC mock galaxies making submm counts ANN vs GRASIL – GALFORM z=0 catalogue

37. ANN vs GRASIL for ABC – comparison for galaxy counts 70  m 100  m 160  m 350  m250  m500  m

38. SED and SPH galaxy models: GRASIL3D A.Schurer 09 PhD theses 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 cells 1° Application : P-DEVA (Serna & Dominguez-Tenreiro) + GRASIL3D

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

40. z = 3.5 z = 2 z = 0

41. Images: face on at z=0

42. Images: Edge on at z=0

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

44. 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