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The Wind in the Machine: Using Machine Learning to Probe Quasar Outflows

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 galaxy’s 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. I’ll 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.
 

Cody Hall, AB 107

Sarah Gallagher (Western University)

November 15, 2017
14:00 - 15:00