ASteCA - Automated Stellar Cluster Analysis
  We present ASteCA (Automated Stellar Cluster Analysis), a suit of tools
designed to fully automatize 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 it's official site.
