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21 st century astrophysicists are confronted with the herculean task ofdistilling the maximum scientific return from extremely expensive and complexspace- or ground-based instrumental projects. This paper concentrates in themining of the time series catalog produced by the European Space Agency Gaiamission, launched in December 2013. We tackle in particular the problem ofinferring the true distribution of the variability properties of Cepheid starsin the Milky Way satellite galaxy known as the Large Magellanic Cloud (LMC).Classical Cepheid stars are the first step in the so-called distance ladder: aseries of techniques to measure cosmological distances and decipher thestructure and evolution of our Universe. In this work we attempt to unbias thecatalog by modelling the aliasing phenomenon that distorts the truedistribution of periods. We have represented the problem by a 2-levelgenerative Bayesian graphical model and used a Markov chain Monte Carlo (MCMC)algorithm for inference (classification and regression). Our results withsynthetic data show that the system successfully removes systematic biases andis able to infer the true hyperparameters of the frequency and magnitudedistributions.

Bayesian Unbiasing of the Gaia Space Mission Time Series Database