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Elodie GIOVANNOLI Laboratoire d’Astrophysique de Marseille, FRANCE Advisor : Veronique BUAT

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

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Elodie GIOVANNOLI Laboratoire d’Astrophysique de Marseille, FRANCE Advisor : Veronique BUAT

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  1. 15/12/2009 ESF conference, Obergurgl Spectral energy distribution modelingfrom UV to 70µm for LIRGsat z=0.7 ElodieGIOVANNOLI Laboratoired’Astrophysique de Marseille, FRANCE Advisor : Veronique BUAT Collaborators : Denis Burgarella, Stefan Noll

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

  3. LIRGs' characteristics (LuminousInfrared Galaxies) 1011 < LIR , L< 1012 Population detectedat 24 µm isdominated by LIRGsat 0.5≤z≤1.0 Plot : At z≈1, IR-Luminous galaxies appears to beresponsible for 70% of the comoving IR energydensity. REF: Le Floc’h et al. 05 Caputi et al. 07 Magnelli et al. 09 Roghiero et al. 09 Comoving IR energy density Le Floc’h et al. 05 ULIRGs LIRGs Low luminosity galaxies Study of LIRGs to understand the formation and evolution of galaxies from z=1.

  4. Description of the sample Sample of 181 LIRGs<z>=0.70 +/- 0.05 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 InvestigatingGALaxyEmission * SED-fitting CIGALE code developpedatLAM-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 (χ2) + bayesiananalysis (close to Kauffmann et al. 2003). *http://www.oamp.fr/cigale/ • *For now, onlydownloading the code is possible but a more sophisticated interface willbe in place at the end of February 2010. OUTPUT :input parameters + M, SFR, Ldust

  6. SFR=SFR0.e-(t/tau) Stellar populations: • Populations synthesis codes • Marastonet al. (2005)(including TP-AGB stars) • PEGASE SFR0 t1 t2 age Combination of a young + an old stellar population with exponentially decreasing SFR at different rates. SFR Dust attenuation: Calzetti et al. (2000) AGN contribution : AGN templates, Siebenmorgen&Krugel 2004 IR models: Dale & Helou (2002) models, parametrised by the factor α, related to the ratio f60/f100 α : power lawslope of the dust mass distribution over heatingintensity Wavelength, µm

  7. Application to the LIRGssample : preliminaryresults of the bayesiananalysis Number of galaxies LogMstar, M LogLdust,L Log SFR,Myr-1 Age of ySP,Gyr Fraction of ySP Fraction of AGN

  8. Application to the LIRGssample : preliminaryresults of the bayesiananalysis Number of galaxies LogMstar, M LogLdust,L Log SFR,Myr-1 Age of ySP,Gyr Fraction of ySP Fraction of AGN Fraction of IR Luminosityreprocessed by dustheated by an AGN.

  9. AGN detection Total sample 9 objectsidentified 26 objectsidentified 49 objectsidentified Brand et al. 2006 Stern et al. 2005 Code CIGALE 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. The mid-IRslope Samplewith a detectionat 70μm, no AGNs L24/L70higherthanpredicted by models. In agreement with Zheng et al. 2007, stackinganalysis The mid-IRslopebrings informations on the fit of IR libraries. Dusttemperature ? Association of a dusttemperaturefollowingthesemodelswillgiverather cold galaxies

  11. The mid-IRslope Samplewith a detectionat 70μm, no AGNs The AGN contamination istooweak to inducesuch an increase of νLν24μm/νLν70μm observed. The local SED templates are not well-suited to fit fluxes from distant galaxies. SeeSymeonidis et al. 2009 The mid-IRslopebrings informations on the fit of IR libraries. Dusttemperature ? Association of a dusttemperaturefollowingthesemodelswillgiverather cold galaxies

  12. SFR density Strong contribution to the star formation activitybeyond z≈0.7 Weexpectactively star forming galaxies Magnelli et al. 2009 ULIRGs LIRGs Normal galaxies

  13. The relation SFR/Mass • Characteristics: • Millenium simulations underestimate the SFR Mstar> 1011 M: in good agreement with semi analyticlmodelsfromBuat et al.08 and Noeske et al. 07 • Mstar< 1011 M: • in good agreement with Santini et al. 09 • red area: unexepectedhigh SFR , SFR/SFRmodels ~5 • 94% of the sampleisactivelystar-bursting : • M > 2.0.1010

  14. Summary & perspectives Our results show that CIGALE is able to fit SED from UV to FIR Getreadyforthcoming Herschel data Improvment of the code to providea valuable and friendlytool to interprete the future data of Herschel : HeRMES consortium (The Herschel Multi-tieredExtragalactic Survey) - Add IR libraries : Chary&Elbaz, Siebenmorgen&Krugel - Add AGN templates : accuratemeasure of the fraction of AGN Fit of the IR counterpartthank to several black bodies Accurate estimation of the dusttemperature Evidence for a hot/cold population athighredshift?

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