Inference in the Milky Way in the Gaia era

We employ state-of-the-art statistical inference and Machine Learning techniques to understand the formation and evolution history of our Galaxy, the Milky Way, using data from the astrometric Gaia mission and ground-based spectroscopic surveys. We first investigate the vertical metallicity gradients of five mono-age stellar populations for a sample of 18,435 dwarf stars selected from the cross-matched Tycho- Gaia Astrometric Solution (TGAS) and RAdial Velocity Experiment (RAVE) Data Release 5. We find an increasingly steeper negative vertical metallicity gradient for the older stellar populations and a steadily increasing intrinsic dispersion in metallicity with age. These results are consistent with a scenario that thin disc stars formed from a flaring thin star-forming disc. To further study the chrono-chemo- dynamical structure of the Galactic disc, we develop a Bayesian Machine Learning framework called BINGO (Bayesian INference for Galactic archaeOlogy), which is a Bayesian Neural Network trained on asteroseismic age data, to obtain accurate relative stellar age estimates with reliable uncertainties for the Apache Point Obser- vatory Galactic Evolution Experiment (APOGEE) stars. After carefully architecting a training set to minimise bias, we apply BINGO to a stellar sample consisting of 17,305 carefully selected evolved stars. We find that the outer disc follows a differ- ent chemical evolution pathway than the inner disc. The outer metal-poor stars only starting to form after the compact thick disc formation phase has completed in the inner region and the star-forming gas disc extended outwardly with a metal-poor gas accretion. Using the Gaia DR2 data, we also try to find dwarf galaxies in eight Fermi-LAT extended, unassociated, gamma-ray source fields, to test the hypothesis that they owe to dark matter annihilation. After probing previously unexplored heliocentric distances of less than 20 kpc with an extreme-deconvolution technique, we find no sign of a dwarf galaxy in any of these fields despite Gaia’s excellent astrometric accuracy