Speaker: Dr. Jennifer Li, University of Illinois at Urbana Champaign

Abstract: Driven by accretion onto the central supermassive black holes (SMBH), stochastic variability from Active Galactic Nuclei (AGN) are encoded with the geometry and dynamics of AGN's innermost regions. Continuum reverberation mapping (CRM) measures the time delay in the variability across different photometric bands to constrain both accretion disk structure and SMBH properties. However, CRM has only been applied to a handful of objects to date due to the stringent observing requirements and large computation time associated with model fitting. In this talk, I will present a fast and flexible deep-learning framework for CRM and the upcoming Vera C. Rubin Observatory’s Legacy Survey of Space and Time (LSST). This framework will be useful in estimating physical parameters for the thousands of AGN monitored with LSST, paving the way for new insights into AGN physics and the evolution of SMBH.

Biographical Sketch: Jennifer Li received her B.Sc. in Atmospheric Science from National Taiwan University and M.S. and Ph.D. degrees in Astronomy from the University of Illinois at Urbana-Champaign (UIUC). After graduating in 2021, she began her postdoctoral research at the University of Michigan. She later joined the inaugural cohort of the Schmidt AI in Science Fellows at University of Michigan and Michigan Institute for Data Science (MIDAS). Dr. Li is currently a research scientist at the National Center for Supercomputer Applications (NCSA) at UIUC and the NSF-Simons SkAI Institute. Her research interests include active galactic nuclei (AGN), black hole and galaxy evolution, time-domain astronomy, and AI applications for scientific research.