A comparative study of four significance measures for periodicity detection in astronomical surveys

We study the problem of periodicity detection in massive data sets of photometric or radial velocity time series, as presented by ESA's Gaia mission. Periodicity detection hinges on the estimation of the false alarm probability of the extremum of the periodogram of the time series. We consider the problem of its estimation with two main issues in mind. First, for a given number of observations and signal-to-noise ratio, the rate of correct periodicity detections should be constant for all realized cadences of observations regardless of the observational time patterns, in order to avoid sky biases that are difficult to assess. Secondly, the computational loads should be kept feasible even for millions of time series. Using the Gaia case, we compare the FM method of Paltani and Schwarzenberg-Czerny, the Baluev method and the GEV method of Süveges, as well as a method for the direct estimation of a threshold. Three methods involve some unknown parameters, which are obtained by fitting a regression-type predictive model using easily obtainable covariates derived from observational time series. We conclude that the GEV and the Baluev methods both provide good solutions to the issues posed by a large-scale processing. The first of these yields the best scientific quality at the price of some moderately costly pre-processing. When this pre-processing is impossible for some reason (e.g. the computational costs are prohibitive or good regression models cannot be constructed), the Baluev method provides a computationally inexpensive alternative with slight biases in regions where time samplings exhibit strong aliase

Gaia Data Release 3: The first Gaia catalogue of variable AGN

One of the novelties of the Gaia-DR3 with respect to the previous data releases is the publication of the multiband light curves of about 1 million AGN. The goal of this work was the creation of a catalogue of variable AGN, whose selection was based on Gaia data only. We first present the implementation of the methods to estimate the variability parameters into a specific object study module for AGN. Then we describe the selection procedure that led to the definition of the high-purity variable AGN sample and analyse the properties of the selected sources. We started from a sample of millions of sources, which were identified as AGN candidates by 11 different classifiers based on variability processing. Because the focus was on the variability properties, we first defined some pre-requisites in terms of number of data points and mandatory variability parameters. Then a series of filters was applied using only Gaia data and the Gaia Celestial Reference Frame 3 (Gaia-CRF3) sample as a reference.The resulting Gaia AGN variable sample, named GLEAN, contains about 872000 objects, more than 21000 of which are new identifications. We checked the presence of contaminants by cross-matching the selected sources with a variety of galaxies and stellar catalogues. The completeness of GLEAN with respect to the variable AGN in the last Sloan Digital Sky Survey quasar catalogue is about 47%, while that based on the variable AGN of the Gaia-CRF3 sample is around 51%. From both a comparison with other AGN catalogues and an investigation of possible contaminants, we conclude that purity can be expected to be above 95%. Multiwavelength properties of these sources are investigated. In particular, we estimate that about 4% of them are radio-loud. We finally explore the possibility to evaluate the time lags between the flux variations of the multiple images of strongly lensed quasars, and show one case.Comment: 19 pages, 31 figures, 2 table. This paper is part of Gaia Data Release 3 (DR3). In press for A&