Automated classification of HIP variables (Dubath+, 2011)

Details Automated classification of HIP variables (Dubath+, 2011) Automated classification of HIP variables (Dubath+, 2011)

Feb 2012

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2012ycat..74142602d&link_type=abstract

VizieR On-line Data Catalog: J/MNRAS/414/2602. Originally published in: 2011MNRAS.414.2602D

Statistics

Methodology

Models, Stars: Variable, Mk Spectral Classification

Scientific paper

We present an evaluation of the performance of an automated classification of the Hipparcos periodic variable stars into 26 types. The sub-sample with the most reliable variability types available in the literature is used to train supervised algorithms to characterize the type dependencies on a number of attributes. The most useful attributes evaluated with the random forest methodology include, in decreasing order of importance, the period, the amplitude, the V-I colour index, the absolute magnitude, the residual around the folded light-curve model, the magnitude distribution skewness and the amplitude of the second harmonic of the Fourier series model relative to that of the fundamental frequency.
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