1. The star formation history of the Fornax dSph galaxy using the new synthetic CMD code IAC-STAR C. Gallart & A. Aparicio (IAC) R. Zinn (U. Yale) F. Pont (Obs. Genève) E. Hardy (NRAO) R. Buonanno & G. Marconi (Obs. Rome)

2. CMD studies by Stetson et al. 1998 Buonanno et al. 1999 Saviane et al. 2000 Gallart et al. 2002 Fornax dSph M T = 7.0x10 7 M sun L V = 1.5 x10 7 L sun dist = 140 Kpc R C = 0.5 Kpc

3. VLT FORS1 image compared to WFPC2 VLT FORS1 data, 6.8 arcmin 1850 sec V 4600 sec I seeing  0.6” Gallart et al. 2003, in prep

4. The Fornax VLT CMDs

6. Computing a synthetic CMD Ingredients Computing method m  IMF Montecarlo Interpolation in the stellar evolution models Bolometric corrections Age, metallicity and mass Luminosity and temperature Magnitudes and colors t  t CEL Z q  Binaries SFR

7. Antonio Aparicio Antonio Aparicio Carme Gallart Carme Gallart Sebastian Hidalgo Sebastian Hidalgo Instituto de Astrofísica de Canarias http://www.iac.es/iac-star.html

8. IAC-STAR is offered by the Stellar Populations in Galaxies research group at La Laguna, Canary Island, Spain (Instituto de Astrofísica de Canarias and University of La Laguna).Instituto de Astrofísica de Canarias This web page and the facility is maintained by the Servicios Informáticos del IAC Servicios Informáticos IAC-STAR SYNTHETIC COLOR-MAGNITUDE DIAGRAM COMPUTATION ALGORITHM Run IAC-STARun IAC-STAR General Information Retrieve paper by Aparicio & Gallart (2003) Feedback and contacting IAC-STAR Acknowledging using IAC-STAR Instituto de Astrofísica de Canarias C/ Vía Láctea s/n, tfno.: +34 922 605 200, fax: +34 922 605 210 www.iac.es www.iac.es

10. IAC-STAR: star formation rate

11. 7, 0, 3, 3, 6, 6, 9, 12, 0, 0, 2, 2, 1, 1, 3

13. IAC-STAR: metallicity law

15. IAC-STAR Stellar Evolution: (Bertelli et al. 1994) Very low masses: (Brocato et al. 1998) TP-AGB extrapolation (Marigo et al. 1998) Bolometric Corrections: Lejeune et al. 1997

16. IAC-STAR OUTPUT Run limits: max saved stars, max computed stars, control filter [0,11] -> (logL)UBVRIJHKL(L)M, min log(L) or max mag saved: 300000 12000000 5 4.00 Age range: 0.100E+09 0.147E+11 SFR law normalyzed to its total integral Normalized time: 0.000000E+00 0.100000E+01 0.100000E+01 0.100000E+01 Normalized time: 0.000000E+00 0.100000E+01 0.100000E+01 0.100000E+01 Lower metallicity law: Z_0, Z_f, mu_f, alfa, lambda: 0.100000E-03 0.100000E-03 0.000000E+00 0.000000E+00 0.000000E+00 Upper metallicity law: Z_0, Z_f, mu_f, alfa, lambda: 0.200000E-02 0.200000E-02 0.000000E+00 0.000000E+00 0.000000E+00 Effective yields for lower and upper laws: 0.0000E+00 0.0000E+00 Binaries: fraction number and minimal mass ratio: 0.0000 0.5000 File heading: log(L), log(Teff), log(g), mass_ini, mass_fin, idem for the secondary; age, Z, mass_2/mass_1, Mbol, U, B, V, R, I, J, H, K, L, L`, M See file bottom for integrated quantities 0.505 3.841 4.243 0.996 0.996 -9.999 -9.999 -9.999 0.000 0.000 0.5828E+10 0.0011 0.000 3.477

17. Total star numbers: Total masses and luminosities: Int(SFR), mass in now alive stars, mass in remnants, Sum[log(L)], sum[log(L**2)] Integrated magnitudes: bolometric and in the used filters Integrated magnitudes: bolometric and in the used filters, obtained from squared luminosities (SBF) 10697919 9812852 300000 0 0 0 10697919 9812852 300000 0 0 0 0.527368E+07 0.315720E+07 0.442533E+06 0.526105E+07 0.549078E+10 0.527368E+07 0.315720E+07 0.442533E+06 0.526105E+07 0.549078E+10 -12.063 ……. -12.063 ……. IAC-STAR

19. GOING BACK TO THE FORNAX STAR FORMATION HISTORY: Work in progress…

20. Starting point: Create synthetic CMDs with  CMD with constant SFR: 13 to 0 Gyr  A number of test Z(t)  Binary fractions: f=0.1, 0.3, 0.6, 0.9; q=0.6  3 IMFs: Kroupa et al. + steeper & shallower  +/- 0.1 in distance modulus and simulate observational errors on them Z(t)

22. Tests with known star formation histories z9b25fkr

24. The comparison allows us to discard a number of Z(t)

27. Coleman, da Costa, Bland- Hawthorn, Martinez-Delgado, Freeman & Malin : Shell structure in the Fornax dSph galaxy, AJ, sub

28. VLT FORS1 Spectra of Fornax dSph, Pont et al. 2003, AJ,in press I < 17.5 40 min 0.8” seeing 1.06 Å/pix  120 stars

29. VLT FORS1 Spectra of Fornax dSph, Pont et al. 2003, AJ, in press I < 17.5 40 min 0.8” seeing 1.06 Å/pix  120 stars

31. Solving the SFH  Metallicity  Except for very simple cases, the metallicity law should be a function to be determined.  Ideally, it should be a free function to be derived together with the SFR (Cole et al. 1999; Holtzman et al. 1999; Harris & Zaritsky 2001)  At least (including less accurate data), Z(t) can be reasonably constrained - Current metallicity may be estimated from spectroscopy of HII regions - It must be compatible with the position of relevant evolutionary phases (RGB, RSG, MS) in the CMD - Z(t) may be assumed to be an increasing function of t

32. Solving the SFH  Crowding + real (external) errors  Crowding should be characterized for each observational data set  Internal (e.g. ALLSTAR’s) errors may not be good representation of real (external errors)  Detailed characterization of crowding from extensive artificial star test is a best choice (Aparicio et al. 1996)

33. Solving the SFH: parameterizing Aparicio et al. (1997) Dolphin (1997) Gallart et al. (1999) Cole (1999) Holtzman et al. (1999) Harris & Zaritsky (2001) (“partial” model)

34. The color-magnitude diagram  Color-magnitude diagram (CMD): best tool to obtain the SFH Shortcomings: Good CMDs can only be obtained for nearby galaxies Local Group and vicinity Advantages: Information on stars of all ages Best case: CMDs reaching oldest main-sequence turnoffs

35. Solving the SFH: parameterizing Partial model: Observations:

36. Solving the SFH: parameterizing 0.5<t<0.6 Gyr Z=0.006 binaries: 25%

37. Solving the SFH: parameterizing 0.5<t<0.6 Gyr Z=0.006 binaries: 25%

38.  Fits based on stars counts in a blind (uniform) grid or on point-to-point fits may be biased by stellar evolution models artifacts (in poorly understood phases) or uncontroled observational errors.  CMD fits based on stars counts in an “intelligently chosen” grid in the CMD may be biased by the choice of the grid. Some criticism: problems and limitations

40. Solving the SFH  Ingredients Data Deep CMD of the galaxy Stellar evolution theory Computing synthetic CMDs for any SFH SFH Method for comparison of CMDs