Improving Automatic Detection of Variable Stars

Details Improving Automatic Detection of Variable Stars Improving Automatic Detection of Variable Stars

Jan 2012

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2012aas...21914505d&link_type=abstract

American Astronomical Society, AAS Meeting #219, #145.05

Astronomy and Astrophysics

Astronomy

Scientific paper

The Minimum Entropy (ME) method described by Cincotta, et al. (1995) is a useful and efficient technique for identification of periodic signals in sparse time-series data. Its original form, however, struggles to correctly identify periods in the high-cadence time-series data with diurnal data gaps common to ground-based transit surveys. We developed a Modified Minimum Entropy (MME) technique to alleviate this problem and tested it with light curves from the KELT transit survey. We find that MME correctly recovers the periods of numerous variable types despite the uneven phase sampling of our test light curves. We further developed a related method to robustly remove outlier data points from phased variable light curves. Together, these techniques may improve variable object yields from the vast data stores of modern high-cadence ground-based photometric surveys.

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