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)

CMD studies by Stetson et al Buonanno et al Saviane et al Gallart et al 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

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

The Fornax VLT CMDs

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

Antonio Aparicio Antonio Aparicio Carme Gallart Carme Gallart Sebastian Hidalgo Sebastian Hidalgo Instituto de Astrofísica de Canarias

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.: , fax:

IAC-STAR: star formation rate

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

IAC-STAR: metallicity law

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

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: Age range: 0.100E E+11 SFR law normalyzed to its total integral Normalized time: E E E E+01 Normalized time: E E E E+01 Lower metallicity law: Z_0, Z_f, mu_f, alfa, lambda: E E E E E+00 Upper metallicity law: Z_0, Z_f, mu_f, alfa, lambda: E E E E E+00 Effective yields for lower and upper laws: E E+00 Binaries: fraction number and minimal mass ratio: 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 E

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) E E E E E E E E E E …… ……. IAC-STAR

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

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)

Tests with known star formation histories z9b25fkr

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

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

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

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

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

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)

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)

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

Solving the SFH: parameterizing Partial model: Observations:

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

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

 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

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