Speakers
Prof.
A. KovacevicProf.
D. Ilic
Description
this lecture illustrates how we can build an AI-driven framework that reconstructs accretion flow transfer functions, SMBH physical parameters, and red-noise variability directly from AGN light curves without assuming stationarity or predefined parametric models. We will show how a data-riven approach allows to infer accretion flow structure and variability mechanisms across diverse AGN populations, enabling scalable characterization of SMBH accretion across millions of AGN in upcoming large time-domain surveys.
Authors
Prof.
A. Kovacevic
Prof.
D. Ilic
