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A Supervised Learning Approach for Exploring Low-Mass Satellite Quenching Beyond the Local Group

Presentation #123.06 in the session Evolution of Galaxies II.

Published onJun 29, 2022
A Supervised Learning Approach for Exploring Low-Mass Satellite Quenching Beyond the Local Group

Our current understanding of the physical mechanisms responsible for suppressing star formation in low-mass satellite galaxies remains incomplete. This is in part driven by the inability of current spectroscopic data sets to robustly characterize the star-formation activity and local environment of faint systems beyond the Local Group. At the same time, there exists an ever growing archival treasure trove from deep photometric surveys that capture these faint systems. The work presented in this talk aims to bridge the observational gap by utilizing the deep photometry in Stripe 82 of the Sloan Digital Sky Survey in conjunction with statistical background subtraction and supervised learning to ultimately study the suppression of star formation in low-mass satellite galaxies in the local Universe. This statistically-driven approach enables us to push beyond the limits of existing spectroscopic data sets, measuring the satellite quenched fraction down to satellite stellar masses of ~107 M in group environments (Mhalo=1013-14 h-1 M) while successfully reproducing existing measurements based on spectroscopic observations.

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