The Star Formation History of Late-Type Galaxies Roberto Cid Fernandes UFSC – Florianópolis -Brasil

Flori-where?

Outline 1- What & Why? 2 – Who & How? 3 – So what? Scope & disclaimer Motivations 2 – Who & How? Schools, Ingredients & Methods: - Based on indices - Based on full spectral fits 3 – So what? Miscelaneous results Caveats

1 – What & Why Mission Impossible! optical spectroscopy the small print Two of the characteristics we expect of these reviews are the need to present a very balanced overview of the theme, in a manner which informs the audience without burying it in excessive technicalities. We are confident that you will be able to fulfil these criteria Mission Impossible! Focus on: optical spectroscopy stellar photons (ie, no em. line SFH diagnostics) late-type (= non-ellipticals) applications to large samples (= SDSS)

1 – Why study stellar pops in galaxies?  To learn about galaxy evolution: SFR(t), Z*(t), cosmology... SFH(t) of the Universe Mass assembly history of a late type galaxy Heavens 04 Mathis 06

1 – Why study stellar pops in galaxies?  To clean starlight pollution from my spectra! Tremonti 04 Li 05

1 – Why study stellar pops in galaxies?  To clean starlight pollution from my spectra! z ~ 0.7 composite Scattered Broad Hb in a Sey 2 Savaglio 05 CF 04

2 – How? Spectral synthesis of integrated stellar populations: “...a subject with bad reputation. Too much has been claimed, and too few have been persuaded.” (Searle, 1986) Basic Recipe (a) Discrete x continuous representations (b) Observables: Indices x Full spectrum (c) From observables to SFH: Methods, methods & methods...

= S ’s (+ gas + dust + ...) Fgal(l) = S F*(l) 2 – How? The Fundamental Theorem of Population synthesis: = S ’s (+ gas + dust + ...) Individual Stars Observed clusters Model SSPs Continuous models Fgal(l) = S F*(l) + extinction x 10-0.4 A(l) & kinematics x LOSVD (v*,s*,vrot)

2a – How? The discrete approach ≈ S SSPj j = 1...N Empirical Pop. Synthesis: SSP = Observed Clusters x1 + x2 + x3 + ... : Stellar evol, spectra & IMF given by Mother Nature : Incomplete coverage of (t,Z) space & l-range Bica 88, Schmidt 91, Ahumada 06... + Pelat 97, 98, CF 01 (math)

2a – How? The discrete approach ≈ S SSPj j = 1...N SSP = Model “clusters” from evolutionary synthesis S xj FSSP(l ; tj,Zj ; IMF, tracks, libs...) population vector : Wider coverage of (t,Z) space & l-range : Models are always models... Models: GALAXEV, SED, STABURST99, PEGASE, Maraston, ...

2a – How? The discrete approach WARNING: Models look great, but there are LOTS of assumptions & tricks in this business! - tracks, - spectral libraries, - interpolation schemes, - ... Models: GALAXEV, SED, STABURST99, PEGASE, Maraston, ...

2a – How? The continuous approach SFR(t) : More general than S bursts : Need to parametrize SF history & chemical evol. : Models are always models... Fritze-v. Alvensleben 06, Bicker 04, ...

2b – Observables: Indices x Full Spectrum Compare Index(t,Z,SFH) models to data to constrain SFH parameters. Instantaneous Bursts Continuous SF Kauffmann 03, ...

2b – Observables: Indices x Full Spectrum Nolan 06 Rectified spectrum (“high pass”) Mathis 06 CF 05

2b – Observables: Indices x Full Spectrum Reichardt 01 Walcher 06 Mayya 06

2c – How? From Observables to SFH... Hypothese space (“priors”) Only 1 Z? Z = Z(t)? Al = ? Dust geometry? Al(t,Z)? Kinematics? Which basis? (clusters, models,...) Which parameters? WARNING: Impossible to review all combinations! Will browse through a few examples Observables space Parameter space Method Brute force discrete grid search? Convex-algebra? Markov-Chains? PCA? AI-techniques? Compression on input or output? Comparions to library of models? How to deal with degeneracies?

2c – How? From Observables to SFH... Pelat 97, 98, Moultaka 00, 04 A very elegant method, yet largely overlooked because (?) of complex math (convex algebra) & few applications. Observables: 2 EWs Parameters: 5 light fractions Reconstructed spectrum Boisson 00

2c – How? From Observables to SFH... 5 indices: D4000, Hb, Hd+Hg, [MgFe]’ & [Mg2Fe] Bayesian comparison to a large library of models Gallazzi 05 PDF of light weighted mean age

2c – How? From Observables to SFH... F(l) fitting with MOPED Multiple Optimized Parameter Estimation & Dta cmpsn Mass & Z in N ~ 10 time-bins M* Mass assembly histories: M(t) Panter 03 Mathis 06

2c – How? From Observables to SFH... F(l) fitting with STARLIGHT - Light (Mass) in N ~ 100 time & Z SSPs - Compress output Downsizing M* M(t) & Z(t) of Star-Forming galaxies Pop. vector = SFH CF 04. 05, 06, Mateus 06, Asari 07

2c – How? From Observables to SFH... UCBD galaxies Corbin 06

2c – How? From Observables to SFH... Many other methods! STEllar Content via Maximum A Posteriori – Ocvirk 05 + Koleva + ... Active Instance-Based Machine Learning – Solorio 05 Bayesian Latent Variable modelling – Nolan 06 Principal Component Analysis – Li 05, Wild Direct fitting – Tadhunter 05, Holt 06, Moustakas 06 ... + MacArthur... Brute Force – Bush 01, 02, 03, 04, 05, 06, 07, 08 ... Diversity in: Math / elegance / speed 1000 “Technicalities” (masks, kinematics, extinction, ...) Physical ingredients Input & Output ...

3 – So What? A few miscelaneous results (a) Global relations: Synthesis parameters X other things: <t>, <Z>, <SFR>, <SSFR>, ... Zgas, M*, Mdyn, environment, ... (b) Daring one step further: SFH(t)! (c) Sanity checks Caveats (d) Closing words

<Z> & <t> x Mass 3a – Global relations Z(gas) x Mass <Z> x Z(gas) <t> x Z(gas) <t> x Z(gas) <Z> x Z(gas) <Z> & <t> x Mass M* Tremonti 04, Gallazzi 05 CF 05, Mateus 06, Asari 07

3b – Going one step further: Evolution AGN SF Z(gas) Idea: Dissect the SFH = SFR(t) & Z*(t) along the left wing of the Seagull (normal SF galaxies)

3b – Going one step further: Evolution Result Low Z(gas) galaxies are much slower in their mass assembly and chemical evolution

3b – Going one step further: Evolution M* Mass assembly histories: M(t) M* SFR(t)/Vol Panter 06 Downsizing Mathis 06

3c – Sanity checks: good news  Different ingreedients yield ~ similar result !! Panter 06 SFR(Synt) ~ SFR(Ha) Asari 07

3c – Sanity checks: good news  Ha/Hb Nebular exctinction – NaD ISM

3c – Sanity checks: good news  Ha/Hb AV (Balmer) ~ 2 AV (Stellar)

a–enhancement is not only an E-gal problem... 3c – Warning:  Ellipticals a–enhancement is not only an E-gal problem... SF-galaxies a Asari 07 Sodre 05

AZD still present in full spectral fits 3c – Warning:  AZD still present in full spectral fits CF 05, Gomes 05

3c – Residuals: ~ Within errors, but ... Ellipticals SF-galaxies Hb–troff: Low amplitude, but systematic. ~ 100 Myr pops. STELIB?

3 – So What? Conclusions (a) Ingreedients & methods have matured a lot! (b) Global properties <t>, <Z>, SFR, SSFR, ... in very good shape  (b) Evolution ... Looking good! (c) Caveats & Future a/Fe issue Realistict dust models ...

Spectral synthesis of integrated stellar populations: “ Spectral synthesis of integrated stellar populations: “...a subject with bad reputation. Too much has been claimed, and too few have been persuaded.” (Searle, 1986)

(At least we managed to fool Scott!) Spectral synthesis of integrated stellar populations: “...a subject with a not so bad reputation anymore. By not claiming too much, we’re now able to convince quite a few people.” (At least we managed to fool Scott!) (A bunch of us, 2006)

Public version of STARLIGHT + results for 572581 SDSS galaxies soon @ www.starlight.ufsc.br & www.inaoep.mx

M*, m*, t* & Z* x nebular Z ... etc, etc & etc!

STARLIGHT & its many applications HE0450-2958 – The “homeless” QSO CaII Triplet velocity dispersions Merritt et al 2006 Vega 2004, Garcia-Rissman et al 2005

2 – The SF-History of Sey 2 nuclei CF, Gu, Melnick, Terlevich2, Kunth, Rodrigues Lacerda, Joguet 2004, MNRAS Strong FC in this Sey 1  79 galaxies 65 Sey 2s ~ 200 pc Base = BC03 + FC

6 – Synthesis of 582k SDSS galaxies N Asari, J Gomes, W Schoenell, J P Papaqui (UFSC) A Mateus (IAG), L Sodré (IAG) & G Stasinska (Meudon) The SEAGal Collaboration: Semi-Empirical Analysis of Galaxies