  Photometric measurements are prone to systematic errors presenting a
challenge to low-amplitude 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 1m-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/h, which is
efficient in detecting variability on time-scales longer than the typical time
interval between observations. Variable objects have larger 1/h 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.

Instrumentation and Methods for Astrophysics;Solar and Stellar Astrophysics