In this work we address the problem of estimating the probabilities of causal contacts between civilizations in the Galaxy. We make no assumptions regarding the origin and evolution of intelligent life. We simply assume a network of causally connected nodes. These nodes refer somehow to intelligent agents with the capacity of receiving and emitting electromagnetic signals. Here we present a three-parametric statistical Monte Carlo model of the network in a simplified sketch of the Galaxy. Our goal, using Monte Carlo simulations, is to explore the parameter space and analyse the probabilities of causal contacts. We find that the odds to make a contact over decades of monitoring are low for most models, except for those of a galaxy densely populated with long-standing civilizations. We also find that the probability of causal contacts increases with the lifetime of civilizations more significantly than with the number of active civilizations. We show that the maximum probability of making a contact occurs when a civilization discovers the required communication technology.

Searches for periodic variable stars are susceptible to seasonal aliases caused by sampling. Nightly observations from ground based telescopes produce a large number of false detections at the integer multiples of day−1 frequency. Here we discuss the case of VISTA Variables in the Vía Láctea-VIrac VAriable Classification Ensemble (VVV-VIVACE) ID 533558, classified by the VIVACE using machine learning as an EA/EB binary with a period of P = 0.6659 day, in contradiction with a previous classification as an AB-type RR Lyrae with period P = 0.4992 day by the OGLE survey. We discuss the problem of phase coverage causing the misclassification with a wrong period by the VVV, in spite of a robust set of 652 data points over six years of observations. As automatic light-curve classifications of large data sets become more popular, this is a problem that cannot be overlooked.