Astrophysics Seminar: Machine learning methods applied to the Dark Energy Survey

Abstract:

he interplay between active galactic nuclei (AGNs) and their host galaxies provides important clues for the physics of both AGNs and galaxies. Current observations reveal a wealth of information regarding the morphology and star-formation rates of galaxies that host AGNs. I will discuss the challenges and difficulties in interpreting these observations, and will show evidence that semi-analytic models (SAMs) have important benefits in studying various modes of AGN accretion. I will then present a specific SAM that offers a simple interpretation to a wide range of observations. In this model, AGNs are triggered by both minor and major merger events, with an accretion that is proportional to the star-formation burst triggered by the merger. I will further discuss various predictions of the model that could help in putting tighter constraints on the growth modes of AGNs.

Seminar Organiser: Prof. Rennan Barkana