Variable stars identification in digitized photographic data

Abstract

We identify 339 known and 316 new variable stars of various types among 250000 lightcurves obtained by digitizing 167 30 × 30 cm photographic plates of the Moscow collection. We use these data to conduct a comprehensive test of 18 statistical characteristics (variability indices) in search for the best general-purpose variability detection statistic. We find that the highest peak on the DFT periodogram, interquartile range, median absolute deviation, and Stetson's L index are the most efficient in recovering variable objects from the set of photographic lightcurves used in our test.