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

2. Flori-where?

3. 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

4. 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)

5. 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

6. 1 – Why study stellar pops in galaxies?
 To clean starlight pollution from my spectra!
Tremonti 04
Li 05

7. 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

8. 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...

9. = 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 A(l)
& kinematics x LOSVD (v*,s*,vrot)

10. 2a – How? The discrete approach
≈ S SSPj j = 1...N
Empirical Pop. Synthesis: SSP = Observed Clusters
x x x
: Stellar evol, spectra & IMF given by Mother Nature
: Incomplete coverage of (t,Z) space & l-range
Bica 88, Schmidt 91, Ahumada Pelat 97, 98, CF 01 (math)

11. 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, ...

12. 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, ...

13. 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, ...

14. 2b – Observables: Indices x Full Spectrum
Compare Index(t,Z,SFH) models to data to constrain SFH parameters.
Instantaneous Bursts
Continuous SF
Kauffmann 03, ...

15. 2b – Observables: Indices x Full Spectrum
Nolan 06
Rectified spectrum (“high pass”)
Mathis 06
CF 05

16. 2b – Observables: Indices x Full Spectrum
Reichardt 01
Walcher 06
Mayya 06

17. 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?

18. 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

19. 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

20. 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

21. 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 , 06, Mateus 06, Asari 07

22. 2c – How? From Observables to SFH...
UCBD galaxies
Corbin 06

23. 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 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
...

24. 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

25. <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

26. 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)

27. 3b – Going one step further: Evolution
Result
Low Z(gas) galaxies are much slower in their mass assembly and chemical evolution

28. 3b – Going one step further: EvolutionM*
Mass assembly histories: M(t) M*
SFR(t)/Vol
Panter 06
Downsizing
Mathis 06

29. 3c – Sanity checks: good news 
Different ingreedients yield ~ similar result !!
Panter 06
SFR(Synt) ~ SFR(Ha)
Asari 07

30. 3c – Sanity checks: good news 
Ha/Hb
Nebular exctinction – NaD ISM

31. 3c – Sanity checks: good news 
Ha/Hb
AV (Balmer) ~ 2 AV (Stellar)

32. 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

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

34. 3c – Residuals: ~ Within errors, but ...
Ellipticals
SF-galaxies
Hb–troff: Low amplitude, but systematic. ~ 100 Myr pops. STELIB?

35. 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
...

36. 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)

37. (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)

38. Public version of STARLIGHT + results for SDSS galaxies &

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

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

42. 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

43. 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