ASteCA - Automated Stellar Cluster Analysis

G.I. PERREN; R.A. VÁZQUEZ; A.E. PIATTI

ASTRONOMY AND ASTROPHYSICS

EDP SCIENCES S A

Lugar: Paris; Año: 2015 vol. 576 p. 1 - 29

0004-6361

We present ASteCA (Automated Stellar Cluster Analysis), a suit of tools designed to fully automatize the standard tests applied onstellar clusters to determine their basic parameters. The set of functions included in the code make use of positional and photometricdata to obtain precise and objective values for a given cluster?s center coordinates, radius, luminosity function and integrated colormagnitude, as well as characterizing through a statistical estimator its probability of being a true physical cluster rather than a randomoverdensity of field stars. ASteCA incorporates a Bayesian field star decontamination algorithm capable of assigning membershipprobabilities using photometric data alone. An isochrone fitting process based on the generation of synthetic clusters from theoreticalisochrones and selection of the best fit through a genetic algorithm is also present, which allows ASteCA to provide accurate estimatesfor a cluster?s metallicity, age, extinction and distance values along with its uncertainties.To validate the code we applied it on a large set of over 400 synthetic MASSCLEAN clusters with varying degrees of field star contam-ination as well as a smaller set of 20 observed Milky Way open clusters (Berkeley 7, Bochum 11, Czernik 26, Czernik 30, Haffner11, Haffner 19, NGC 133, NGC 2236, NGC 2264, NGC 2324, NGC 2421, NGC 2627, NGC 6231, NGC 6383, NGC 6705, Ruprecht1, Tombaugh 1, Trumpler 1, Trumpler 5 and Trumpler 14) studied in the literature. The results show that ASteCA is able to recovercluster parameters with an acceptable precision even for those clusters affected by substantial field star contamination.ASteCA is written in Python and is made available as an open source code which can be downloaded ready to be used from it?s officialsite.