The star formation history of the

Fornax dSph galaxyusing the

new synthetic CMD codeIAC-STAR

C. Gallart & A. Aparicio (IAC)

R. Zinn (U. Yale)

F. Pont (Obs. Genève)

E. Hardy (NRAO)

R. Buonanno & G. Marconi (Obs. Rome)

Fornax dSphMT= 7.0x107 MsunLV = 1.5 x107 Lsundist = 140 Kpc RC = 0.5 Kpc

CMD studies byStetson et al. 1998Buonanno et al. 1999Saviane et al. 2000Gallart et al. 2002

VLT FORS1image

comparedto

WFPC2

VLT FORS1 data, 6.8 arcmin1850 sec V 4600 sec Iseeing ≈ 0.6”Gallart et al. 2003, in prep

The Fornax VLT CMDs

Computing a synthetic CMD

Ingredients

t

ψ

SFR m

φ

IMF

β

tCEL

Z

qBinaries

Computing method

Montecarlo Age, metallicity and mass

Interpolation in the stellar evolution models Luminosity and temperature

Bolometric corrections Magnitudes and colors

Antonio AparicioAntonio AparicioCarme GallartCarme GallartSebastianSebastian HidalgoHidalgo

Instituto de Astrofísica de CanariasInstituto de Astrofísica de Canarias

http://http://www.iac.eswww.iac.es//iaciac--star.htmlstar.html

Instituto de Astrofísica de CanariasC/ Vía Láctea s/n, tfno.: +34 922 605 200, fax: +34 922 605 210

www.iac.es

•Run IAC-STAR •General Information•Retrieve paper by Aparicio & Gallart (2003) •Feedback and contacting IAC-STAR •Acknowledging using IAC-STAR

IAC-STARSYNTHETIC COLOR-MAGNITUDE DIAGRAM COMPUTATION

ALGORITHM

IAC-STAR is offered by the Stellar Populations in Galaxies research group atLa Laguna, Canary Island, Spain (Instituto de Astrofísica de Canarias and

University of La Laguna).This web page and the facility is maintained by the Servicios Informáticos del IAC

IAC-STAR: star formation rate

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

IAC-STAR: metallicity law

StellarStellar EvolutionEvolution::((BertelliBertelli et al. 1994)et al. 1994)

VeryVery lowlow massesmasses: : (Brocato et al. 1998)(Brocato et al. 1998)

TPTP--AGB AGB extrapolationextrapolation((MarigoMarigo et al. 1998)et al. 1998)

BolometricBolometric CorrectionsCorrections::LejeuneLejeune et al. 1997et al. 1997

IAC-STAR

IAC-STAR

OUTPUTOUTPUTRunRun limitslimits: : maxmax savedsaved starsstars, , maxmax computedcomputed starsstars, control , control filterfilter [0,11] [0,11] --> > ((logL)UBVRIJHKL(L)MlogL)UBVRIJHKL(L)M, , minmin log(L) log(L) oror maxmax magmag savedsaved: 300000 12000000 5 4.00: 300000 12000000 5 4.00AgeAge rangerange: 0.100E+09 0.147E+11: 0.100E+09 0.147E+11SFR SFR lawlaw normalyzednormalyzed toto itsits total integraltotal integralNormalizedNormalized time: 0.000000E+00 0.100000E+01 0.100000E+01 0.100000E+01time: 0.000000E+00 0.100000E+01 0.100000E+01 0.100000E+01

LowerLower metallicitymetallicity lawlaw: Z_0, Z_f, : Z_0, Z_f, mu_fmu_f, alfa, lambda: 0.100000E, alfa, lambda: 0.100000E--03 0.100000E03 0.100000E--03 03 0.000000E+00 0.000000E+00 0.000000E+000.000000E+00 0.000000E+00 0.000000E+00UpperUpper metallicitymetallicity lawlaw: Z_0, Z_f, : Z_0, Z_f, mu_fmu_f, alfa, lambda: 0.200000E, alfa, lambda: 0.200000E--02 0.200000E02 0.200000E--02 02 0.000000E+00 0.000000E+00 0.000000E+000.000000E+00 0.000000E+00 0.000000E+00EffectiveEffective yieldsyields forfor lowerlower andand upperupper lawslaws: 0.0000E+00 0.0000E+00: 0.0000E+00 0.0000E+00BinariesBinaries: : fractionfraction numbernumber andand minimalminimal massmass ratio: 0.0000 0.5000ratio: 0.0000 0.5000File File headingheading: log(L), : log(L), log(Tefflog(Teff), log(g), ), log(g), mass_inimass_ini, , mass_finmass_fin, , idemidem forfor thethe secondarysecondary; ; ageage, Z, , Z, massmass_2/_2/massmass_1, _1, MbolMbol, U, B, V, R, I, J, H, K, L, L`, M, U, B, V, R, I, J, H, K, L, L`, MSeeSee file file bottombottom forfor integratedintegrated quantitiesquantities

0.505 3.841 4.243 0.996 0.996 0.505 3.841 4.243 0.996 0.996 --9.999 9.999 --9.999 9.999 --9.999 0.000 0.000 0.5828E+10 9.999 0.000 0.000 0.5828E+10 0.0011 0.000 3.477 0.0011 0.000 3.477

IAC-STAR

Total star numbers:Total star numbers:Total massesTotal masses andand luminositiesluminosities:: IntInt(SFR), mass in now (SFR), mass in now alive stars, mass in remnants, Sum[log(L)], sum[log(L**2)]alive stars, mass in remnants, Sum[log(L)], sum[log(L**2)]Integrated magnitudesIntegrated magnitudes: : bolometric and in the used filtersbolometric and in the used filtersIntegrated magnitudes: bolometric and in the used filters, Integrated magnitudes: bolometric and in the used filters, obtained fromobtained from squared luminosities (SBF)squared luminosities (SBF)

10697919 9812852 300000 0 0 010697919 9812852 300000 0 0 00.527368E+07 0.315720E+07 0.442533E+06 0.526105E+070.527368E+07 0.315720E+07 0.442533E+06 0.526105E+07

0.549078E+100.549078E+10--12.063 …….12.063 …….

GOING BACK TO THE FORNAXSTAR 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 modulusand simulate observational errors on them

Z(t)

∑=i

jii

j MAM α

∑ −=

jj

2jj2

OOMχ )(

Tests with known star formation historiesz9b25fkr

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

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

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

VLT FORS1 Spectra of Fornax dSph,Pont et al. 2003, AJ,in press

I < 17.540 min 0.8” seeing 1.06 Å/pix ≈120 stars

VLT FORS1 Spectra of Fornax dSph,Pont et al. 2003, AJ, in press

I < 17.540 min 0.8” seeing 1.06 Å/pix ≈120 stars

Solving the SFHg Metallicity g Except for very simple cases, the metallicity law should be a function to be determined.

g 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)

g 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

g Crowding + real (external) errors

g Crowding should be characterized for each observational data set

g Internal (e.g. ALLSTAR’s) errors may not be good representation of real (external errors)

g Detailed characterization of crowding from extensive artificial star test is a best choice (Aparicio et al. 1996)

Solving the SFH: parameterizing

iS)]();([ tZtSψ≡SFH (“partial” model)

Aparicio et al. (1997)

Dolphin (1997)

Gallart et al. (1999)

Cole (1999)

Holtzman et al. (1999)

Harris & Zaritsky (2001)

The color-magnitude diagram

g Color-magnitude diagram (CMD): best tool to obtain the SFH

Advantages:

• Information on stars of all ages

• Best case: CMDs reaching oldest main-sequence turnoffs

Shortcomings:

• Good CMDs can only be obtained for nearby galaxies Local Group and vicinity

Solving the SFH: parameterizingiSPartial model: j

iMjOObservations:

∑=i

iiSAZS αψ ),(

∑=i

jii

j MAM α

∑ −=

jj

2jj2

OOMχ )(

Solving the SFH: parameterizing

iSj

iM

0.5<t<0.6 Gyr

Z=0.006

binaries: 25%

Solving the SFH: parameterizing

iSj

iM

0.5<t<0.6 Gyr

Z=0.006

binaries: 25%

Some criticism: problems and limitations

g 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.

gCMD fits based on stars counts in an “intelligently chosen” grid in the CMD may be biased by the choice of the grid.

Solving the SFH

g Ingredients

Deep CMD of the galaxy

Data

Stellar evolution theory

Computing synthetic CMDs for any SFH

Method for comparison of CMDs

SFH