Research summary
Dark matter makes up most of the universe’s mass. Yet its fundamental properties remain a mystery. To shine a light on these, Dr. Yashar Hezaveh, Canada Research Chair in Astrophysical Data Analysis and Machine Learning, is using strong gravitational lensing—the bending of light by massive objects—to map dark matter distribution on sub-galactic scales.
To achieve this, he and his research team are developing machine learning techniques tailored for high-dimensional astrophysical data, enabling more precise analysis than ever before. Their work is making use of unprecedented datasets from next-generation surveys like Euclid and the 10-year Legacy Survey of Space and Time, offering the potential for ground-breaking discoveries about dark matter particles and their microphysics. By combining cutting-edge artificial intelligence with astrophysics, their work could deepen our understanding of the universe’s most elusive matter while strengthening Canada’s leadership in data-driven space science.