  We present a new method to discriminate periodic from non-periodic
irregularly sampled lightcurves. We introduce a periodic kernel and maximize a
similarity measure derived from information theory to estimate the periods and
a discriminator factor. We tested the method on a dataset containing 100,000
synthetic periodic and non-periodic lightcurves with various periods,
amplitudes and shapes generated using a multivariate generative model. We
correctly identified periodic and non-periodic lightcurves with a completeness
of 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 the
model using these synthetic lightcurves and applied the method on the EROS-2
dataset. A crucial consideration is the speed at which the method can be
executed. Using hierarchical search and some simplification on the parameter
search we were able to analyze 32.8 million lightcurves in 18 hours on a
cluster of GPGPUs. Using the sensitivity analysis on the synthetic dataset, we
infer that 0.42% in the LMC and 0.61% in the SMC of the sources show periodic
behavior. The training set, the catalogs and source code are all available in
http://timemachine.iic.harvard.edu.
