Enabling data science in the Gaia mission archive: The present-day mass function and age distribution

In this paper, we present and contextualize these challenges by building two probabilistic models using Hierarchical Bayesian Modelling. These models represent a key challenge in astronomy and are of paramount importance for the Gaia mission itself. Moreover, we approach the implementation by leveraging a generic distributed processing engine through an existing software package for Markov chain Monte Carlo sampling. The two computationally intensive models are then validated with simulated data in different scenarios under specific restrictions, and their performance is assessed to prove their scalability. We argue that this approach will not only serve for the models in hand but also for exemplifying how to address similar problems in science, which may need to both scale to bigger data sets and reuse existing software as much as possible. This will lead to shorter time to science in massive data archives.