Physics Colloquium: Machine Learning for Dark Matter

Speaker: Dr. Bryan Ostdiek of UC Santa Cruz

Abstract: There is five times more dark matter than ordinary matter in the universe, but we have almost no idea what it is. To learn about the possible interactions of dark matter, physicists use complementary data from cosmological probes, astroparticle observations, and particle colliders. There is an increasing need for advanced analytics and machine learning to process these vastly growing datasets. This talk details examples using machine learning in each of the three realms. First, I demonstrate using image recognition techniques on images of strongly lensed galaxies to constrain dark matter properties. Second, I use machine learning to uncover the phase space distribution of dark matter near the Earth, which directly impacts the interpretation of direct detection experiments. Finally, I examine how unsupervised learning methods can aid collider searches for dark matter. The talk concludes with comments on the intersection of machine learning and physics.