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Register a new userAn efficient approach to extract parameters from star cluster CMDs: fitCMD
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Charles Bonatto
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This work presents an approach (fitCMD) designed to obtain a comprehensive set of astrophysical parameters from colour-magnitude diagrams (CMDs) of star clusters. Based on initial mass function (IMF) properties taken from isochrones, fitCMD searches for the values of total (or cluster) stellar mass, age, global metallicity, foreground reddening, distance modulus, and magnitude-dependent photometric completeness that produce the artificial CMD that best reproduces the observed one; photometric scatter is also taken into account in the artificial CMDs. Inclusion of photometric completeness proves to be an important feature of fitCMD, something that becomes apparent especially when luminosity functions are considered. These parameters are used to build a synthetic CMD that also includes photometric scatter. Residual minimization between the observed and synthetic CMDs leads to the best-fit parameters. When tested against artificial star clusters, fitCMD shows to be efficient both in terms of computational time and ability to recover the input values.
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In the last decades we witnessed an increase in studies of open clusters of the Galaxy, especially because of the good determination for a wide range of values of parameters such as age, distance, reddening, and proper motion. The reliable determination of the parameters strongly depends on the photometry available and especially on the U filter, which is used to obtain the color excess E(B-V) through the color-color diagram (U-B) by (B-V) by fitting a zero age main-sequence. Owing to the difficulty of performing photometry in the U band, many authors have tried to obtain E(B-V) without the filter. But because of the near linearity of the color-color diagrams that use the other bands, combined with the fact that most fitting procedures are highly subjective (many done by eye) the reliability of those results has always been questioned. Our group has recently developed, a tool that performs isochrone fitting in open-cluster photometric data with a global optimization algorithm, which removes the need to visually perform the fits and thus removes most of the related subjectivity. Here we apply our method to a set of synthetic clusters and two observed open clusters (Trumpler 1 and Melotte 105) using only photometry for the BVRI bands. Our results show that, considering the cluster structural variance caused only by photometric and Poisson sampling errors, our method is able to recover the synthetic cluster parameters with errors of less than 10% for a wide range of ages, distances, and reddening, which clearly demonstrates its potential. The results obtained for Trumpler 1 and Melotte 105 also agree well with previous literature values.
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