Comparative performance of selected variability detection techniques in photometric time series data
Abstract
Photometric measurements are prone to systematic errors presenting a challenge to lowamplitude variability detection. In search for a generalpurpose variability detection technique able to recover a broad range of variability types including currently unknown ones, we test 18 statistical characteristics quantifying scatter and/or correlation between brightness measurements. We compare their performance in identifying variable objects in seven time series data sets obtained with telescopes ranging in size from a telephoto lens to 1 mclass and probing variability on timescales from minutes to decades. The test data sets together include light curves of 127 539 objects, among them 1251 variable stars of various types and represent a range of observing conditions often found in groundbased variability surveys. The real data are complemented by simulations. We propose a combination of two indices that together recover a broad range of variability types from photometric data characterized by a wide variety of sampling patterns, photometric accuracies and percentages of outlier measurements. The first index is the interquartile range (IQR) of magnitude measurements, sensitive to variability irrespective of a timescale and resistant to outliers. It can be complemented by the ratio of the lightcurve variance to the mean square successive difference, 1/η, which is efficient in detecting variability on timescales longer than the typical time interval between observations. Variable objects have larger 1/η and/or IQR values than nonvariable objects of similar brightness. Another approach to variability detection is to combine many variability indices using principal component analysis. We present 124 previously unknown variable stars found in the test data.
 Publication:

Monthly Notices of the Royal Astronomical Society
 Pub Date:
 January 2017
 DOI:
 10.1093/mnras/stw2262
 arXiv:
 arXiv:1609.01716
 Bibcode:
 2017MNRAS.464..274S
 Keywords:

 methods: data analysis;
 methods: statistical;
 stars: variables: general;
 Astrophysics  Instrumentation and Methods for Astrophysics;
 Astrophysics  Solar and Stellar Astrophysics
 EPrint:
 29 pages, 8 figures, 7 tables