Selecting Quasars by their Intrinsic Variability

TL;DR

This paper introduces a variability-based method for selecting quasars using a power-law model of their light-curve structure function, demonstrating high completeness and purity across different surveys and redshift ranges.

Contribution

The authors develop and validate a simple, effective variability-based quasar selection technique that performs comparably or better than traditional color-based methods, especially at challenging redshifts.

Findings

Achieves 90-96% purity and 90% completeness in quasar selection.

Outperforms color selection in the redshift range 2.5<z<3.

Remains effective with sparse data, such as in Pan-STARRS 1 mock observations.

Abstract

We present a new and simple technique for selecting extensive, complete and pure quasar samples, based on their intrinsic variability. We parametrize 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 ~60 epochs of imaging data, taken over ~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 6 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.