Cosmic Origins
Active Galactic Nuclei Science Interest GroupWe invite everyone to join us for our AGN SIG Vision Series. The goal of the Vision Series is to highlight forward-looking, community-driven perspectives on AGN science. This quarterly series will consist of a 30-minute “vision” talk focused on outstanding questions surrounding supermassive black holes, their origin, growth, and connection to galaxy evolution, followed by ample time for community discussion.
AGN SIG about AGN SIG Vision Series
Location
Virtual
Dates
26 May 20261:00pm ET / 10:00am PT
Community
AGN SIG
Type
Seminar
AGN Science in the Era of Big Data: From DESI to the AI Revolution
Speaker
Stéphanie Juneau (NSF NOIRLab, NSF-Simons CosmicAI)
Abstract
As we advance deeper into the data-rich era, AGN and galaxy co-evolution studies are entering a vast discovery space fueled by massive multi-wavelength datasets, offering us powerful statistical leverage for population studies. With the Dark Energy Spectroscopic Instrument (DESI) already delivering unprecedented catalogs and upcoming surveys like Rubin LSST and Roman on the horizon, the challenge is shifting from data acquisition to advanced analysis techniques and astrophysical interpretation. In this talk, I will highlight recent breakthroughs from DESI, including the most extensive census of black holes in dwarf galaxies and the identification of rare, short-lived phases such as changing-look AGN and extreme broad-line quasars. I will also present recent work on mapping ionized gas to probe AGN feedback with the James Webb Space Telescope (JWST), demonstrating how this approach provides a crucial high-redshift complement to current surveys. Lastly, I will discuss how the integration of multi-wavelength datasets and Machine Learning/AI is redefining AGN identification and guiding our vision to address long-standing questions about black hole/galaxy co-evolution.
Seminar Connection
Zoom Registration, you will receive connection details by email after registration: https://zoom.us/j/97689874465?pwd=XZNUqr19oopa1LfD7hgln1aQ2vbnT2.1
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