Abstract

Contributed Talk - Splinter AGN   (MW-1250)

Machine Learning Decomposition of Photometric AGN Variability

Iliana Cortés, Nikolaos Gianniotis, and Kai Lars Polsterer
Heidelberg Institute for Theoretical Studies (HITS)

We present the results of applying MAGNOLIA, a Gaussian Process (GP) framework, to model the variability of active galactic nuclei (AGN) optical light curves. AGN are among the most luminous objects in the Universe, powered by accretion onto supermassive black holes (SMBHs) at their centres. Their UV-optical emission originates primarily from two regions: the accretion disk (AD) and the broad-line region (BLR). The BLR reprocesses radiation from the AD, producing a delayed response whose timescale depends on the SMBH mass. In preparation for upcoming large-area surveys such as LSST, we demonstrate that the time delay between the AD and BLR can be recovered using photometric monitoring supplemented by a single-epoch spectrum. We validate our methodology using a sample obtained from a cross-match between the Australian Dark Energy Survey (OzDES) spectroscopic observations and the Stripe 82 Sloan Digital Sky Survey (SDSS) photometric data.