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[ascl:1512.019] UPSILoN: AUtomated Classification of Periodic Variable Stars using MachIne LearNing

UPSILoN (AUtomated Classification of Periodic Variable Stars using MachIne LearNing) classifies periodic variable stars such as Delta Scuti stars, RR Lyraes, Cepheids, Type II Cepheids, eclipsing binaries, and long-period variables (i.e. superclasses), and their subclasses (e.g. RR Lyrae ab, c, d, and e types) using well-sampled light curves from any astronomical time-series surveys in optical bands regardless of their survey-specific characteristics such as color, magnitude, and sampling rate. UPSILoN consists of two parts, one which extracts variability features from a light curve, and another which classifies a light curve, and returns extracted features, a predicted class, and a class probability. In principle, UPSILoN can classify any light curves having arbitrary number of data points, but using light curves with more than ~80 data points provides the best classification quality.

Code site:
https://github.com/dwkim78/upsilon
Described in:
http://adsabs.harvard.edu/abs/2016A%26A...587A..18K
Bibcode:
2015ascl.soft12019K

Views: 1527

ascl:1512.019
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