A machine learning lens on galaxy mergers in the nearby Universe

Abstract: 

Merger events are thought to interrupt the otherwise gradual evolution of galaxies with short periods of intense change, simultaneously transforming galaxy morphologies, fueling bursts of star formation, and giving rise to high accretion rates onto central supermassive black holes. In this talk, I will demonstrate how machine vision techniques have fundamentally changed the way mergers are identified and studied and highlight new observational results that constrain the influence of mergers on the evolutionary trajectory of galaxies. While some of these results highlight the elegance of the current paradigm for hierarchical galaxy growth, others point to complications brought on by the multi-wavelength diversity of galaxies, stars, and supermassive black holes in the Universe.

Bio: 

Bobby Bickley is an astronomy Ph.D. candidate at the University of Victoria (unceded Lekwungen territory, Victoria BC). He also holds a B.Sc. in mechanical engineering from the University of Connecticut. Bobby’s research interests are galaxy evolution, galaxy mergers, supermassive black hole & galaxy co-evolution, and the application of machine learning techniques to problems in astronomy.