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 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.

The Hubble Catalog of Variables (HCV)

“…Taking into account the heterogeneous nature of the input HSC v3 data, we tested various statistical indicators of variability ("variability indices", Section 1.2), which characterize the overall scatter of measurements in a light curve and/or degree of correlation between consecutive flux measurements. Sokolovsky et al (2017b) presented a detailed description and comparison of 18 variability indices proposed in the literature. These indices were tested on seven diverse sets of groundbased photometric data containing a large number of known variables.…”

New insights into time series analysis

“…The interquartile range (IQR - Kim et al 2014) is also included in our analysis since it was reported as one of the best statistical parameters to select variable stars (Sokolovsky et al 2017). The IQR uses the inner 50% of measurements, excluding the 25% brightest and 25% faintest flux measurements, i.e.…”

“…von Neumann 1941(e.g. von Neumann , 1942Welch & Stetson 1993;Stetson 1996;Enoch et al 2003;Kim et al 2014;Sokolovsky et al 2017). Current techniques of data processing can be improved considerably.…”

“…Improvements to statistical parameters for which there are few measurements are crucial to analyze surveys such as PanSTARRS, with a mean of about 12 measurements in each filter, and the extended VVV project (VVVX), between 25 to 40 measurements (Chambers et al 2016;Minniti et al 2010). Sokolovsky et al (2017) tested 18 parameters (8 scatterbased parameters and 10 correlated-based parameters), comparing their performance. According to the authors the correlationbased indices are more efficient in selecting variable objects than the scatter-based indices for data sets containing hundreds of measurement epochs or more.…”

“…acknowledges support from the UK Science and Technology Facilities Council. We thank Maria Ida Moretti who pointed us to the Sokolovsky et al (2017) paper, which was crucial to perform a strict comparison with the most recent results. We also thank the reviewer for his/her thorough review and highly appreciate the comments and suggestions, which significantly contributed to improving the quality of the publication.…”