Astrophysics Seminar: Machine learning methods applied to the Dark Energy Survey
Maayane Soumagnac, UCL
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