Title: Selection of AGNs through variability and correlation.
Authors: Vincenzo Petrecca and Maurizio Paolillo.
Abstract: Variability has proven to be a hallmark of nuclear activity in galaxies, at all frequencies, proving to be an efficient AGN selection tool. This notebook explores the parameter space of variable sources showing that even the simplest statistics obtained from light curves perform very well in the selection of QSOs. We prove that lightcurve variance and correlation among bands alone, allow us to produce samples of QSOs (and AGN) with a completeness of ~ 90%, albeit with low purity (~50%). By adding the extendedness and color, it is possible to remove most of the contaminants reaching a purity ~ 90 % and decreasing the completeness by less than 10%. Correlation analysis among different bands thus enables a very fast and cheap first order selection of candidate QSOs (and possibly less luminous AGN); we propose to include the correlation indexes among the LSST data products, as they could be relevant features for more complex selection approaches based on traditional and Machine Learning methods.