Title: Bayesian Unbiasing of the Gaia Space Mission Time Series Database,
Abstract: 21 st century astrophysicists are confronted with the herculean task of
distilling the maximum scientific return from extremely expensive and complex
space- or ground-based instrumental projects. This paper concentrates in the
mining of the time series catalog produced by the European Space Agency Gaia
mission, launched in December 2013. We tackle in particular the problem of
inferring the true distribution of the variability properties of Cepheid stars
in 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: a
series of techniques to measure cosmological distances and decipher the
structure and evolution of our Universe. In this work we attempt to unbias the
catalog by modelling the aliasing phenomenon that distorts the true
distribution of periods. We have represented the problem by a 2-level
generative Bayesian graphical model and used a Markov chain Monte Carlo (MCMC)
algorithm for inference (classification and regression). Our results with
synthetic data show that the system successfully removes systematic biases and
is able to infer the true hyperparameters of the frequency and magnitude
distributions.