CAREER: Building the Merger Tree of the Milky Way with Machine Learning

Massachusetts Institute Of Technology, Cambridge MA

Investigators

Abstract

Principal Investigator (PI) Necib and her team will search for stellar debris in our galaxy. These stellar structures were formed when galaxies collided with the Milky Way. The team will use artificial intelligence to develop an automated process to complete the difficult task of analyzing observations of more than 1.5 billion stars from the European Space Agency (ESA) mission Gaia. The team will compare their data to simulations to chart the history of the formation of the Milky Way. The PI is committed to broadening participation. Building on her successful Tunisian Podcast Ga3da Falakia, the PI will work with her graduate students to produce a series of podcasts in a range of languages. The PI will also host a series of Gaia Hackathons for undergraduate and high school students. The PI aims to build the merger tree of the Milky Way using both supervised and unsupervised machine learning techniques. The PI will build a clustering algorithm that integrates measurement uncertainties and scales with the increase in data size, as well as an interpretable equivariant graph neural network that predicts the merging times and infall masses of mergers into the Milky Way, trained on Illustris-TNG50, and applied to Gaia DR3/4 data. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.