modelling multiwavelength seds – tools for galaxy formation models plan: * modelling seds -grasil-...

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)

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

T

SSP dttZtTLtSFRTL0

))(,()()(

Semi-empirical: attenuation curve for LIR

+ IR shape. Pros: non time consuming – analysis of large data sets. Cons: not great predictive power

Theoretical: Explicit computation of radiative transfer and dust emissionPros: broader interpretative/predictive power. Cons: time consuming

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 SEDs2) 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

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

Sample of GALEX NUV-selected late type galaxies (Buat+, Iglesias-Paramo+)

MW

SMC •No age-dependent extinction•Sequence of models with increasing dust content (1m 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 e

t al 1

999

UV-bright s

b

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

ESB=Age/e-folding time

ESB=0.2-2peak phaseESB<0.2

early phase

ESB=2-4evolved

ESB>4Post-sb

AGN

cirrus

MC

ff

Sync

~70% SB dominated (Lagn/Lbol<10%)

* Toto Agn subtracted

LIR/Mden for SB=180 Mo/Lo=> M/LHCN=5

Gao & Solomon 04LIR/Mden=90 Mo/Lowith M/LHCN=10 Mo/(K km/s pc2)

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

SAM + GRASIL

|||||||||||SAM+[C&F00 + slab]

Fontanot, Somerville, Silva+09

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

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

Effects of dust assumptions on SED (Schurer+09)

MW-type

Ellipticals

Irregular

Z from chem. model

Mdust/Mgas(t)

M*=10^12

M*=10^11

M*=10^10

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)

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)

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

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

Local universe

GALFORM(Cole+00)+GRASIL Granato+00; Baugh+05; Lacey+08,09

But high-z universe Revised model: reproduce multi- LFs and counts/z-distr w top-heavy IMF in starbursts

850mOld model

850mNew model

SFRMBH(t)/1e5

ṀBH(t)*300

Anti-hierarchical Baryonic Collapse (ABC) + GRASILGranato+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

SCUBA 850 m

MAMBO 1200 m

model

data

K Band counts and z distribution

All sph

Passive sph

modelobserved

K20 SURVEYmass range required by sub-mm

counts

Extremely Red Objects (R-K)>5

passive

active

Modelling SEDs with Artificial Neural NetworksAlmeida+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(100m)>2mJy

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:

Input: parameters determining the SED

Output: SED

ANNalgorithm(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)

ANN & SED: 2 methodsGeneral 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, 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

ANN GALFORM+GRASIL Almeida+09

Quiescent Bursts

log Lann/Lorigvs log Lorig

rest=0.17mz=3 catalogue

obs=850mz=2 catalogue

rest=24mz=0.5 catalogue

Quiescent

Bursts

Quiescent

Bursts

Quiescent

Bursts

z=3 =0.17m

z=0.5 =24m

z=2 =850m

ANN GRASIL Silva+09

Input neurons for star forming Molecular Clouds

2RMC /Rmin2

RMC/L*,MC RMC /Rmin

MC MMC/RMC2

Input neurons for Cirrus

Hardness1.7 Mdust2

/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

ANN vs GRASIL - Examples of single SEDs

M51

M82

ANN vs GRASIL with ABC SAM– randomly extracted SEDs

ANN vs GRASIL - ABCmock galaxies making

submm counts

ANN vs GRASIL – GALFORM z=0 catalogue

ANN vs GRASIL for ABC – comparison for galaxy counts

70m 100m 160m

350m250m 500m

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

z = 3.5 z = 2 z = 0

red – STARSyellow - GAS

t/to

z = 3.5 z = 2 z = 0

Images: face on at z=0

Images: Edge on at z=0

Preliminary tests: z = 2, comparison to SCUBA galaxies

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