Event Details

Astrophysics Webinar

AGN are the most powerful variable sources known and yet our understanding of the mechanisms underpinning the variability remains limited. New time domain surveys are changing this with large data sets of time series and spectra but analyzing these is still a major challenge. Fortunately, advances in machine learning have identified rare examples of extreme variable behavior, ranging from AGN turning off and on to potential merging supermassive black hole binaries. In this talk, I will review some of the different classes in the AGN variability zoo identified with the CRTS and ZTF sky surveys and the new insights they are providing into the dynamic environment of the accretion disk around a supermassive black hole.

If someone from outside of the Astrophysics group would like to join the seminar, please send a request to i.garland@lancaster.ac.uk