Volume 576, April 2015
|Number of page(s)||29|
|Section||Numerical methods and codes|
|Published online||13 March 2015|
ASteCA: Automated Stellar Cluster Analysis
Facultad de Ciencias Astronómicas y Geofísicas (UNLP),
2 Observatorio Astronómico, Universidad Nacional de Córdoba, Laprida 854, 5000 Córdoba, Argentina
3 Consejo Nacional de Investigaciones Científicas y Técnicas, Av. Rivadavia 1917, C1033AAJ, Buenos Aires, Argentina
Received: 6 September 2014
Accepted: 4 December 2014
We present the Automated Stellar Cluster Analysis package (ASteCA), a suit of tools designed to fully automate the standard tests applied on stellar clusters to determine their basic parameters. The set of functions included in the code make use of positional and photometric data to obtain precise and objective values for a given cluster’s center coordinates, radius, luminosity function and integrated color magnitude, as well as characterizing through a statistical estimator its probability of being a true physical cluster rather than a random overdensity of field stars. ASteCA incorporates a Bayesian field star decontamination algorithm capable of assigning membership probabilities using photometric data alone. An isochrone fitting process based on the generation of synthetic clusters from theoretical isochrones and selection of the best fit through a genetic algorithm is also present, which allows ASteCA to provide accurate estimates for 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 contamination as well as a smaller set of 20 observed Milky Way open clusters (Berkeley 7, Bochum 11, Czernik 26, Czernik 30, Haffner 11, Haffner 19, NGC 133, NGC 2236, NGC 2264, NGC 2324, NGC 2421, NGC 2627, NGC 6231, NGC 6383, NGC 6705, Ruprecht 1, Tombaugh 1, Trumpler 1, Trumpler 5 and Trumpler 14) studied in the literature. The results show that ASteCA is able to recover cluster 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 its official site.
Key words: methods: statistical / galaxies: star clusters: general / open clusters and associations: general / techniques: photometric
© ESO, 2015
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