November 27, 2023 to December 1, 2023
Dual node
Europe/Paris timezone

Deciphering Black-Hole Physics with Modern Machine-Learning Methods

Not scheduled
Dual node

Dual node

IAP (Paris) & CCA/Flatiron (New York) IAP 98bis Boulevard Arago 75014 Paris FRANCE CCA/Flatiron 5th Avenue New York (NY) USA
Poster New York Posters


Markus Rau (Argonne National Laboratory)Mr Thaddaeus Kiker (AstroAI CfA Harvard, Columbia University)


Supermassive black holes reside in the center of almost every galaxy. Today's supermassive black holes are mostly dormant (like the one at the center of our Milky Way), but in the past, they were actively accreting large amounts of matter and releasing vast amounts of energy. Galaxies with the brightest, most active supermassive black holes, called active galactic nuclei (AGN), are the most luminous objects in the universe. AGNs show many visible and ultraviolet emission lines, which probe the accreting material's physical conditions and the black hole's properties.

We discuss our work towards building a generative model of AGN spectra that will help us to study correlations between emission lines to derive insight into the accretion process, starting from a model to cluster AGN spectra directly using spectral input.

Primary author

Mr Thaddaeus Kiker (AstroAI CfA Harvard, Columbia University)


Dr James Steiner (AstroAI CfA Harvard) Dr Joanna Kuraszkiewicz (AstroAI CfA Harvard) Markus Rau (Argonne National Laboratory)

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