Comparing Age and Mass Determinations from 100 Million Monte Carlo Simulations to 288 LMC Clusters with CMD Ages

Astronomy and Astrophysics – Astrophysics

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Scientific paper

Traditional Simple Star Population (SSP) models compute the integrated colors of stellar clusters in the infinite mass limit, where colors are independent of mass and the stellar mass function is fully populated. However, real clusters have a finite mass and an insufficient number of stars to fully populate the mass function, particularly among the high mass stars. Our MASSCLEAN SSP models offer a more realistic representation, as they provide mass-dependent integrated colors and include the effect of stochastic fluctuations for finite mass clusters. We've completed 100 million Monte Carlo simulations to create a database revealing the dispersion of integrated colors observed in LMC-like clusters. Our MASSCLEANage application uses this database with the observed integrated photometry of LMC clusters to solve, simultaneously, for mass and age. Our MASSCLEAN ages compare very well to CMD ages found for the 288 LMC clusters studied, far better than methods using χ2 minimization fit to traditional SSP models in the infinite mass limit. We find the χ2 minimization method will produce an excess of very young and old clusters, and a deficit of clusters in the log(age/yr)=[7.0,7.5] range, as compared to CMD ages. While the age of massive clusters with colors close to the infinite mass limit are often estimated well by the traditional method, the sometimes very red and often low mass clusters can lead to many catastrophic errors in age determination. Integrated colors are the only available measurement for age and mass determination for unresolved or partially resolved stellar clusters in The Local Group and beyond. Cluster age and mass are used to estimate a whole range of properties for populations of clusters and galaxies, making accurate values crucial to a broad number of investigations. This material is based upon work supported by the National Science Foundation under Grant No. 0607497-1009550.

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