Our galaxy, the Milky Way, is surrounded by numerous small galaxies and star clusters that can be influenced by its gravitational forces, leading to the formation of stellar streams— celestial “rivers” orbiting around our galaxy. These streams offer a unique opportunity for astronomers to delve into the mysteries of galaxy formation and the elusive nature of dark matter. (For an intriguing example, check out our feature in The Globe & Mail)

Thanks to cutting-edge cosmic surveys, we now have access to comprehensive data on millions of stars in our universe, including their full 6D information (position and velocity). The SUDS Scholar will be at the forefront of developing a Bayesian framework to assess the membership probability of each star in potential streams and to characterize the properties of these stellar streams. This involves leveraging vast astronomical datasets, totaling several gigabytes of data, obtained from one of the largest spectroscopic surveys, the Dark Energy Spectroscopic Instrument (DESI).

In this research project, the SUDS Scholar will explore the development and application of innovative statistical and computational techniques. These methodologies are crucial not only for unraveling the secrets hidden within stellar streams but also for paving the way for future astronomical surveys.

This project aims to develop innovative geometric deep learning methods to identify and characterize stellar streams in the Milky Way. Stellar streams are elongated groups of stars that once belonged to smaller galaxies or star clusters that were disrupted by our galaxy’s gravitational forces. These celestial structures serve as crucial forensic evidence of our galaxy’s formation history and provide unique probes of dark matter’s distribution and properties. We will apply graph neural networks and other geometric deep learning techniques to analyze stellar data from the Gaia satellite, which has mapped the positions and velocities of tens of millions of stars with unprecedented precision. These methods are particularly well-suited for this astronomical challenge as they can naturally capture the spatial and kinematic relationships between stars while handling irregular data structures. The project will also incorporate complementary data from the Dark Energy Spectroscopic Instrument (DESI) survey to enhance our understanding of stellar properties. By developing this novel approach to stellar stream detection, we aim to uncover previously unknown structures and gain deeper insights into the Milky Way’s evolutionary history and dark matter distribution.

Dark matter makes up most of the matter in the Universe, yet it cannot be seen directly—it interacts only through gravity. This project uses artificial intelligence and data science techniques to uncover the hidden structure of dark matter by analyzing the motion of stars in dwarf galaxies and stellar streams. These small galaxies and elongated star systems act as “gravitational detectors,” responding to the unseen dark matter around them. The SUDS Scholar will apply simulation-based inference (SBI), a cutting-edge approach in machine learning that uses simulated data to train neural networks to infer the physical parameters of complex systems. By comparing simulated and real astronomical datasets, the team will learn how to extract the dark matter distribution and test different theories about its properties.This project offers hands-on experience in modern data-driven astrophysics, combining tools from AI, Bayesian statistics, and computational modeling. Students will gain exposure to real astronomical survey data, explore uncertainty estimation, and contribute to developing machine learning models that help us understand one of the Universe’s greatest mysteries.