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 30x30cm 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.