Comparative performance of selected variability detection techniques in photometric time series data

Photometric measurements are prone to systematic errors presenting a challenge to lowamplitude variability detection.In search for a general-purpose 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 m-class and probing variability on time-scales from minutes to decades.The test data sets together include lightcurves of 127539 objects, among them 1251 variable stars of various types and represent a range of observing conditions often found in ground-based 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 time-scale 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 time-scales longer than the typical time interval between observations.Variable objects have larger 1/η and/or IQR values than non-variable 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.