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We present a new method to discriminate periodic from non-periodicirregularly sampled lightcurves. We introduce a periodic kernel and maximize asimilarity measure derived from information theory to estimate the periods anda discriminator factor. We tested the method on a dataset containing 100,000synthetic periodic and non-periodic lightcurves with various periods,amplitudes and shapes generated using a multivariate generative model. Wecorrectly identified periodic and non-periodic lightcurves with a completenessof 90% and a precision of 95%, for lightcurves with a signal-to-noise ratio(SNR) larger than 0.5. We characterize the efficiency and reliability of themodel using these synthetic lightcurves and applied the method on the EROS-2dataset. A crucial consideration is the speed at which the method can beexecuted. Using hierarchical search and some simplification on the parametersearch we were able to analyze 32.8 million lightcurves in 18 hours on acluster of GPGPUs. Using the sensitivity analysis on the synthetic dataset, weinfer that 0.42% in the LMC and 0.61% in the SMC of the sources show periodicbehavior. The training set, the catalogs and source code are all available inhttp://timemachine.iic.harvard.edu.

A Novel, Fully Automated Pipeline for Period Estimation in the EROS 2 Data Set