The Wind in the Machine: Using Machine Learning to Probe Quasar Outflows
Sarah Gallagher
University of Western Ontario
Every massive galaxy hosts a supermassive black hole that grew as a luminous quasar through active accretion when the universe was a fraction of its present age. Quasar activity can therefore be described as an adolescent phase of galaxy evolution: inevitable, difficult, and (perhaps) transformative. This transformation can arise from quasar feedback whereby the fast winds that accompany accretion inject energy into the host galaxys interstellar medium and affect the course of star formation. We have direct views through such winds from the population of quasars with broad absorption lines. The broad absorption lines are complex and diverse, and unpacking the geometry of the wind from a single sightline is challenging. Ill describe two distinct methodologies developed by our group that use the power of unsupervised clustering to organize windy quasars. The resulting classifications offer insight into the mechanisms that generate quasar outflows, essential for understanding their role in galaxy feedback.
Date: Jeudi, 15 mars 2018 Time: 11:30 Where: Université de Montréal Pavillon Roger-Gaudry, Local D-460