1. Elodie GIOVANNOLI Laboratoire d’Astrophysique de Marseille, FRANCE Advisor : Veronique BUAT Collaborators : Denis Burgarella, Stefan Noll Spectral energy distribution modeling from UV to 70µm for LIRGs at z=0.7 15/12/2009 ESF conference, Obergurgl

2. OUTLINE Motivation: accurate estimation of physical parameters, SED-fitting 1. Introduction : LIRGs’ characteristics Description of the sample Description of the sample 2. SED fitting Code CIGALE Code CIGALE http://www.oamp.fr/cigale/ http://www.oamp.fr/cigale/ 3. Application to the LIRGs sample Mid-IR slope Mid-IR slope SFR/Mass SFR/Mass 4. Future task

3. Population detected at 24 µm is dominated by LIRGs at 0.5≤z≤1.0 Plot : At z≈1, IR-Luminous galaxies appears to be responsible for 70% of the comoving IR energy density. REF: Le Floc’h et al. 05 Caputi et al. 07 Magnelli et al. 09 Roghiero et al. 09 Le Floc’h et al. 05 Study of LIRGs to understand the formation and evolution of galaxies from z=1. LIRGs' characteristics ( LIRGs' characteristics (Luminous Infrared Galaxies) ULIRGs LIRGs Low luminosity galaxies Comoving IR energy density 10 11 < L IR, L  < 10 12

4. Description of the sample Sample of 181 LIRGs =0.70 +/- 0.05 Detected at 24µm : f24µm ≥ 83 µJy Detected at 24µm : f24µm ≥ 83 µJy Sub-sample of 62 LIRGS (flux at 70 µm) Selection of the GTO SPITZER/MIPS CDFS (Chandra Deep Field South) (Le Floc’h et al. 2005), cross-correlated with MUSYC (Multiwavelength survey by Yale-Chile) and FIDEL (Far- Infrared Deep Extragalactic Legacy Survey) UV (2310 A) GALEX images U U38 B V R I z J H K MUSYC 3.6 4.5 5.8 8.0 µm CDFS, IRAC 24 and 70 µm CDFS + FIDEL, MIPS 17 filters

5. CIGALE : Code Investigating GALaxy Emission * SED-fitting CIGALE code developped at LAM-Marseille (Burgarella et al. 05, Noll et al. 09) Task: To derive physical galaxy parameters from broad-band UV-to-IR SEDs at given redshifts. INPUT : Photometric broad-bands Star Formation History Fraction of AGN Dust Attenuation IR library AGN templates Fit of the entire spectrum Results : best model ( χ 2 ) + bayesian analysis (close to Kauffmann et al. 2003). *http://www.oamp.fr/cigale/ *For now, only downloading the code is possible but a more sophisticated interface will be in place at the end of February 2010. OUTPUT : input parameters + M , SFR, L dust

6. SFR 0 SFR SFR=SFR 0.e -(t/tau) age Populations synthesis codes Maraston et al. (2005) (including TP-AGB stars) PEGASE Stellar populations: Combination of a young + an old stellar population with exponentially decreasing SFR at different rates. Dust attenuation : Calzetti et al. (2000) IR models: Dale & Helou (2002) models, parametrised by the factor α, related to the ratio f 60 /f 100 α : power law slope of the dust mass distribution over heating intensity Wavelength, µm t1t1 t2t2 AGN contribution : AGN templates, Siebenmorgen&Krugel 2004

7. Application to the LIRGs sample : preliminary results of the bayesian analysis Number of galaxies Log M star, M  Log L dust, L  Log SFR, M  yr -1 Age of ySP, GyrFraction of ySPFraction of AGN

8. Application to the LIRGs sample : preliminary results of the bayesian analysis Number of galaxies Log M star, M  Log L dust, L  Log SFR, M  yr -1 Age of ySP, GyrFraction of ySPFraction of AGN Fraction of IR Luminosity reprocessed by dust heated by an AGN.

9. AGN detection Code CIGALE 49 objects identified Stern et al. 2005 26 objects identified Brand et al. 2006 9 objects identified Total sample After AGN identification: Total sample: 121 objects 70 µm sample : 42 objects Before AGN identification: Total sample: 181 objects 70 µm sample : 62 objects

10. Sample with a detection at 70μm, no AGNs The mid-IR slope brings informations on the fit of IR libraries. Dust temperature ? Association of a dust temperature following these models will give rather cold galaxies L 24 /L 70 higher than predicted by models. In agreement with Zheng et al. 2007, stacking analysis The mid-IR slope

11. Sample with a detection at 70μm, no AGNs The mid-IR slope brings informations on the fit of IR libraries. Dust temperature ? Association of a dust temperature following these models will give rather cold galaxies The mid-IR slope The AGN contamination is too weak to induce such an increase of νLν 24μm /νLν 70μm observed. The local SED templates are not well-suited to fit fluxes from distant galaxies. See Symeonidis et al. 2009

12. Magnelli et al. 2009 SFR density Strong contribution to the star formation activity beyond z≈0.7 We expect actively star forming galaxies LIRGs Normal galaxies ULIRGs

13. Characteristics : Millenium simulations underestimate the SFR M star > 10 11 M  : in good agreement with semi analyticl models from Buat et al.08 and Noeske et al. 07 M star < 10 11 M  : in good agreement with Santini et al. 09 red area: unexepected high SFR, SFR/SFR models ~5 94% of the sample is actively star-bursting : M  > 2.0.10 10 Characteristics : Millenium simulations underestimate the SFR M star > 10 11 M  : in good agreement with semi analyticl models from Buat et al.08 and Noeske et al. 07 M star < 10 11 M  : in good agreement with Santini et al. 09 red area: unexepected high SFR, SFR/SFR models ~5 94% of the sample is actively star-bursting : M  > 2.0.10 10 The relation SFR/Mass

14. Summary & perspectives Our results show that CIGALE is able to fit SED from UV to FIR Get ready forthcoming Herschel data Improvment of the code to provide a valuable and friendly tool to interprete the future data of Herschel : HeRMES consortium Improvment of the code to provide a valuable and friendly tool to interprete the future data of Herschel : HeRMES consortium (The Herschel Multi-tiered Extragalactic Survey) (The Herschel Multi-tiered Extragalactic Survey) - Add IR libraries : Chary&Elbaz, Siebenmorgen&Krugel - Add IR libraries : Chary&Elbaz, Siebenmorgen&Krugel - Add AGN templates : accurate measure of the fraction of AGN - Add AGN templates : accurate measure of the fraction of AGN Fit of the IR counterpart thank to several black bodies Fit of the IR counterpart thank to several black bodies Accurate estimation of the dust temperature Accurate estimation of the dust temperature Evidence for a hot/cold population at high redshift? Evidence for a hot/cold population at high redshift?