Machine learning with Gaia and large stellar surveys

ABSTRACT:

The Gaia mission as well as large-scale ground-based spectroscopic surveys are collecting complex data for millions (even billions) of stars. The community is therefore using more and more machine-learning methods to cope with the amount of data.

In this talk I will present some recent examples of supervised (typically regression) and unsupervised (typically dimensionality reduction and clustering) methods in the context of stellar and Galactic astrophysics. In particular in the field of Galactic open clusters, the combination of Gaia data with machine-learning approaches is revolutionizing a whole field.