Monday, February 10th, 2020 @ 12:30pm
PAA, Room A214
The Influence of Giant Black Holes on the Fate of Galaxies, Stephanie Juneau (NSF’s OIR Lab)
Supermassive black holes – with masses of millions to billions of times that of the Sun – reside in the nuclei of galaxies. While black holes are not directly visible, surrounding material becomes extremely luminous before being accreted, creating telltale signatures of black hole activity. In turn, the amount of activity tells us about black hole growth, and about energy injection back into the host galaxies. This so-called black hole feedback is thought to play a role in regulating the rate at which galaxies form new stars, thereby affecting directly their evolution across cosmic time. After a brief overview, I will highlight new findings from a multi-scale analysis of gas ionization and dynamics thanks to 3D spectroscopy with the VLT/MUSE instrument. I will then present observational constraints on the fueling of black holes, and on the extent to which they can change the fate of galaxies from statistical analyses of large datasets derived from SDSS. The latter are paving the way to yet larger experiments such as the Dark Energy Spectroscopic Instrument (DESI), which will yield over 35 million spectra of galaxies and quasars. I will conclude by briefly showcasing how the Astro Data Lab (datalab.noao.edu) and other science platforms play a role in the analysis of large datasets to further our knowledge on supermassive black holes, galaxies, and beyond.
About Stephanie Juneau
Stephanie joined the NSF’s OIR Lab ASTRO Data Lab team as a staff scientist, coming from a staff scientist position at CEA Saclay in France. She received her PhD in astronomy from the University of Arizona in 2011 under the supervision of the NSF’s OIR Lab’s Mark Dickinson. Her research interests are focused on the evolution of galaxies and supermassive black holes across cosmic time. She brings to the Data Lab team a wealth of experience and ideas in developing and applying new methods for turning large survey data sets into scientific knowledge.