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Modelling SEDs with Artificial Neural Networks

Modelling SEDs with Artificial Neural Networks. Laura Silva (INAF/Trieste) GianLuigi Granato (INAF/Trieste) , Andrew Schurer (INAF/Padova), Cesario Almeida, Carlton Baugh, Cedric Lacey, Carlos Frenk (ICC/Durham). Outline: SED modelling- approaches vs aims

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Modelling SEDs with Artificial Neural Networks

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  1. Modelling SEDs with Artificial Neural Networks Laura Silva (INAF/Trieste) GianLuigi Granato (INAF/Trieste), Andrew Schurer (INAF/Padova), Cesario Almeida, Carlton Baugh, Cedric Lacey, Carlos Frenk (ICC/Durham) • Outline: • SED modelling- approaches vs aims • GRASIL and application to SAM • Modelling SED with ANN

  2. Multi- SED modelling – approaches vs aims *Stellar pop. synthesis *SFR(t)+Mgas(t),Z(t) exponentials, chemical evol. or galaxy formation models * 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 (continuum & nebular lines) 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) • No Monte Carlo

  4. 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 redshift slices; SFR(t), Mgas(t), Z(t), morphology, scale radii for stars & dust • 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 • SAMs with theoretical SED: GALFORM+GRASIL (Granato+00, Silva+01, Baugh+05, Lacey+08)MORGANA+GRASIL(Monaco+07, Fontanot+07, 08) • Anti-hierar.BarionicCollapse+GRASIL(Granato+04, Silva+05,Lapi+06)

  5. Differenttreatmentspredictdifferent SED for the same SFR(t) SAM + GRASIL SAM + [C&F00 + slab] SF histories from the Semi-Analytical Model for galaxy formation MORGANA SED by: *GRASIL (colored) *Empirical [attenuation curve with C&F + slab] (hatched) Fontanot, Somerville, Silva+08

  6. Differenttreatmentspredictdifferent SED for the same SFR(t) SAM+GRASIL SAM+ templates [Chary&Elbaz01, Lagache+04, Devriendt+99] SF histories from SAM MORGANA SED by: *GRASIL (black) *Templates (color) Fontanot, Somerville, Silva+08

  7. GALFORM + GRASIL (Granato+00, Silva+01, Baugh+05, Lacey+08) Local universe : K-band 0.2 m B-band 60 m Revised model: reproduce multi- LFs and counts/ z-distr with top-heavy IMF in starbursts But high-z universe : 850m Old model 850m New model

  8. Improving the computing time: Modelling SEDs with Artificial Neural Neworks Spectral variance for a GALFORM + GRASIL catalogue • SEDs : complex, non-linear, high dimensionality and large variance functions of some galaxy properties =>ANN: Mathematical algorithms for data analysis, introduced to replicate the brain behavior - learn from examples ANN is a black box that is trained to predict the SED from controlling parameters using a suitable precomputed training set (many couples input-output)

  9. Output layer: SED, one unit for each L() Input layer: parametersdetermining the SED wjk nj=wjkik oj=f(nj) Hidden layers: black box! Propagation rule: the output from each unit is weighted and summed to form the input for the upper layer units: nj=wjkik The new output is oj=f(nj) , f=non linear function Learning: the ANN istrainedwith a given target- weights are adjustedto best approximate a given set ofinputs/outputs

  10. ANN & SED: 2 methods “Universal” andvery fast (Silva+08): input = physicalquantitiesdetermining the SED of MCs and Cirrus – one single trained net “Less universal” and super-fast (Almeida+08): input = galaxy properties – re-train the net for different realizations Mstar, Zstar, Zgas, Lbol, vcirc, R1/2 (bulge & disc), V, Mburst, tlast burst … Each simulated catalogue from a SAM requires a trained net << 1 sec -> exploit the whole Millennium Simulation • MCs: Optical depth , R/Rsubl. • 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

  11. Examples: single objects M82 ARP220

  12. Examples: models extracted from ABC SAM (G04, S05) FULL/ANN Tot: black/red MC: dark /light green Cirr: blue/cyan m12.00zv2.50 @ 0.25 Gyr m13.00zv3.50 @ 0.25 Gry m13.20zv3.50 @ 0.1 Gyr m13.20zv3.50 @ 0.5 Gyr

  13. Examples: models extracted from a GALFORM+GRASIL catalogue Almeida et al.

  14. …and one catastrophe ….work in progress….. m12.00zv2.50 @ 0.1 Gyr Improving the reconstructed SED by splitting the output neurons

  15. 100(1-Lpredicted/Loriginal) vs Loriginal 24 m B-band GALFORM+GRASIL catalogue: >70% with error < 10% - MIR and submm have larger variance 850 m Almeida et al.

  16. Colours for z=0 GALFORM+GRASIL catalogue Almeida et al.

  17. ABC SAM – Galaxy counts FULL: red ANN: blue 24 m 850 m

  18. 24 m z=0.5 0.17 m z=3 850 m z=2 GALFORM+GRASIL: Luminosity Functions ______ original - - - - - - recontructed Almeida et al.

  19. Summary • Multi-wavelength modelling as a tool to quantitatively deconvolve/ interpret observations – make predictions/ 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 some applications • For large cosmological applications: promising solution with ANN

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