1. The Cozumel Experiment: Recovery of Star Formation Histories of Simulated Data Sets Jon Holtzman (NMSU) contributors: Andy Dolphin, Jason Harris, David Valls-Gabaud, Xavier Hernandez Robert Wilson Basilio Santiago, Leandro Kerber, Sandro Javiel Emma Nasi, Giampaolo Bertelli

2. Issues ● How well can star formation histories be recovered from color-magnitude diagrams? – Random errors – Systematic errors, in particular, from uncertainties in input assumptions ● What can be determined robustly? – What do we want to know? Some possibilities: Distribution of stellar ages over long timescales, burstiness/duty cycle of SF, accurate dating of specific events, metallicity evolution, constraints on stellar evolution, binaries, IMF, distances, extinction, etc. ● What impact are we currently making?

3. Background ● Many different groups have worked on CMD analysis, using a variety of different techniques, input physics, assumptions (e.g. stellar evolution results). Techniques generally tested with synthetic data, but parameter space that might be tested is very large and may be nontrivial to simulate. Coupled with development to match input simulations, quality of recovered accuracy may be suspect ● Experiment done in 2001 for Coimbra meeting to assess accuracy of recovered SFH from an actual LMC data set. Results “interesting, even though difficult to understand and impossible to quantify”

4. Coimbra experiment results

5. Other comparisons: IC 1613 IC1613 SFH analysis done independently by Tolstoy, Cole, Dolphin (Skillman et al. 2003)

6. ● Scientific results are not “democratic” ● Blind analysis of synthetic data may be useful for understanding uncertainties and their causes ● Largest uncertainties may arise from systematics arising from imperfect assumptions; synthetic test needs to attempt to address this ● Issue: only moderate “buy-in” from SF modelling community on the idea ● Issue: want to assess results from “developed” techniques ● Current experiment “tests the waters”; input desired on utility of extension Motivation for a new synthetic test

7. Simulated data sets ● Nine simulated data sets (3 SSP, 6 with extended SFH) created, roughly modelled after: LMC cluster(s), Fornax GC, Ursa Minor, Fornax, Carina, LMC field. These sample different age ranges of stars. In 2 cases, narrow-field, deeper observations as well as wider-field, shallower observations were simulated. ● Simulation of errors done with fake star files, including non- Gaussian errors ● Some variation of input physics used to avoid preference towards modellers that adopt same inputs. Problem: how much uncertainty is there in input physics, e.g. stellar evolution, atmospheres, IMF, metallicity spread, etc.?? This initial round of tests relatively conservative? ● Caveat: simple comparison not particularly fair, e.g., without consideration of error bars, covariances, etc. ● Caveat: validity depends on fidelity of modeller!

8. Results: SSP simulations Old LMC cluster, HST ● m-M=18.43, A_V=0.32 ● KTG IMF ● Girardi (/Kurucz) models ● 0.35 binary fraction Int. Fornax cluster, HST ● m-M=21.05, A_V=0.1 ● -2.2 IMF ● YY + BaSeL2.2 models ● 0.5 binary fraction Young LMC cluster, HST ● m-M=18.55, AV=0.32 ● -2.6 IMF ● Girardi + BaSeL2.2 models ● 0.4 binary fraction

9. Results: coz 2/3 simulation Ursa Minor simulation ● HST + ground simulations ● m-M=19.45, A_V=0.08 ● Girardi+BaSeL2.0 models ● KTG IMF ● 0.45 binary fraction ● Continuous enrichment Conclusion: ● Accurate dating at oldest ages is difficult (no surprise) ● Metallicity issues important ● Tuning possible?

10. Results: coz 5/6 simulation Fornax field simulation ● HST + ground simulations ● m-M=21.05, A_V=0.1 ● Girardi+BaSeL2.2models ● KTG IMF ● 0.36 binary fraction ● Continuous enrichment, no metallicity spread at fixed age Conclusion: details of SF are difficult to recover (but not necessarily more difficult than recognized by modellers); cumulative SF history more robust.

11. Results: coz 7 Carina field simulation, ground ● M-M=20.15, A_V=0.1 ● Girardi (/Kurucz) models ● KTG IMF ● 0.42 binary fraction ● Continuous metallicity enrichment, with metallicity spread

12. Results: coz 8 LMC field simulation, HST ● M-M=18.55, A_V=0.32 ● Girardi (/Kurucz)models ● -2.80 IMF slope ● 0.32 binary fraction ● Continuous metallicity enrichment, with metallicity spread

13. Results: distances, extinctions,metallicities ● Distances, extinctions may be sensitive to input assumptions ● Deriving metallicity evolution is plausible!

14. Discussion ● Details of SF history may be difficult, but overall age distributional potentially robust ● Quality of fits not specifically resported, but probably better than obtained for real data. Issue: are best fits good fits? Does it matter? ● Potential improvements by addition of data, e.g. abundances, abundance distributions, distances, extinctions. Possible improvement for answering specific questions, e.g. age of an event if external timescale known ● How applicable are these tests for other methods; are these relevant for the whole community?

15. Possible suggestions ● CMD modellers – Perform analysis using multiple inputs – Specifically discuss age resolution, crosstalk – Present cumulative age distribution ● Stellar modellers – Provide data in homogeneous format to allow cross- comparison. – Address interpolation issues ● Community/referees – Encourage modellers to investigate systematics, present meaningful quantitative results – Encourage additional blind testing??

16. Multiple analyses of same data

17. How can we make an impact? ● What is useful for constraining galaxy evolution? ● Cumulative stellar age distributions for range of galaxies ● Burstiness/duty cycle may be more difficult, consider young systems (e.g. Dohm-Palmer et al. 98 for GR8, Pegasus DIG, Leo A, Sextans A). ● Chemical evolution?? ● Impact will be larger if we can increase sample: work on more luminous, and more distant, systems