We study the problem of periodicity detection in massive data sets of photometric or radial velocity time series, as presented by ESAs Gaia mission. Periodicity detection hinges on the estimation of the false alarm probability (FAP) of the extremum o
f 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. Second, the computational loads should be kept feasible even for millions of time series. Using the Gaia case, we compare the $F^M$ method (Paltani 2004, Schwarzenberg-Czerny 2012), the Baluev method (Baluev 2008) and the GEV method (Suveges 2014), 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 aliases.
The ESA Gaia mission will provide a multi-epoch database for a billion of objects, including variable objects that comprise stars, active galactic nuclei and asteroids. We highlight a few of Gaias properties that will benefit the study of variable ob
jects, and illustrate with two examples the work being done in the preparation of the data processing and object characterization. The first example relates to the analysis of the nearly simultaneous multi-band data of Gaia with the Principal Component Analysis techniques, and the second example concerns the classification of Gaia time series into variability types. The results of the ground-based processing of Gaias variable objects data will be made available to the scientific community through the intermediate and final ESA releases throughout the mission.
