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 light curves of 127 539 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 light-curve variance to the mean square successive difference, 1/eta, which is efficient in detecting variability on time-scales longer than the typical time interval between observations. Variable objects have larger 1/eta 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.