Research summary
The nature of dark matter and the origin of dark energy are two of the biggest unsolved mysteries of modern cosmology-but a new generation of telescopes is poised to produce colossal volumes of data to tackle these questions in the coming years. Analyzing these new data using traditional statistical methods will be challenging. Artificial intelligence (AI) and machine learning offer alternatives.
As Canada Research Chair in Computational Cosmology and Artificial Intelligence, Dr. Laurence Perreault-Levasseur is looking for ways to use machine learning to simulate and analyze cosmological data. More specifically, she and her research team are measuring the Hubble constant with strong gravitational lensing and machine learning. They are also reconstructing the initial conditions of the Universe with survey data and AI to map the initial three-dimensional Universe. Ultimately, they hope to enable rapid, precise, accurate analyses of upcoming observations from large sky surveys.