Selecting Quasars by Their Intrinsic Variability

We present a new and simple technique for selecting extensive, complete, and pure quasar samples, based on their intrinsic variability. We parameterize the single-band variability by a power-law model for the light-curve structure function, with amplitude A and power-law index {gamma}. We show that quasars can be efficiently separated from other non-variable and variable sources by the location of the individual sources in the A-{gamma} plane. We use {approx}60 epochs of imaging data, taken over {approx}5 years, from the SDSS stripe 82 (S82) survey, where extensive spectroscopy provides a reference sample of quasars, to demonstrate the power of variability as a quasar classifier in multi-epoch surveys. For UV-excess selected objects, variability performs just as well as the standard SDSS color selection, identifying quasars with a completeness of 90% and a purity of 95%. In the redshift range 2.5 < z < 3, where color selection is known to be problematic, variability can select quasars with a completeness of 90% and a purity of 96%. This is a factor of 5-10 times more pure than existing color selection of quasars in this redshift range. Selecting objects from a broad griz color box without u-band information, variability selection in S82 can afford completeness and purity of 92%, despite a factor of 30 more contaminants than quasars in the color-selected feeder sample. This confirms that the fraction of quasars hidden in the 'stellar locus' of color space is small. To test variability selection in the context of Pan-STARRS 1 (PS1) we created mock PS1 data by down-sampling the S82 data to just six epochs over 3 years. Even with this much sparser time sampling, variability is an encouragingly efficient classifier. For instance, a 92% pure and 44% complete quasar candidate sample is attainable from the above griz-selected catalog. Finally, we show that the presented A-{gamma} technique, besides selecting clean and pure samples of quasars (which are stochastically varying objects), is also efficient at selecting (periodic) variable objects such as RR Lyrae.