Accelerating white dwarf science with machine learning

Olivier Vincent ( Université de Montréal )

The study of white dwarf stars is on the verge of a revolution, thanks to upcoming spectroscopic surveys promising to increase the number of confirmed objects by an order of magnitude. The Gaia survey recently released about 100,000 low-resolution white dwarf spectra, representing nearly three times the previous number of stars with spectroscopic observation. In this talk, I present an analysis of this new sample done with machine learning algorithms, and how it pushes the frontier of our understanding of white dwarf evolution and their use as probes of the age of our galaxy.