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Title: A machine learning approach to measuring the quenched fraction of low-mass satellites beyond the Local Group
ABSTRACT Observations suggest that satellite quenching plays a major role in the build-up of passive, low-mass galaxies at late cosmic times. Studies of low-mass satellites, however, are limited by the ability to robustly characterize the local environment and star formation activity of faint systems. In an effort to overcome the limitations of existing data sets, we utilize deep photometry in Stripe 82 of the Sloan Digital Sky Survey, in conjunction with a neural network classification scheme, to study the suppression of star formation in low-mass satellite galaxies in the local Universe. Using a statistically driven approach, we are able 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⊙). At high satellite stellar masses (≳1010 M⊙), our analysis successfully reproduces existing measurements of the quenched fraction based on spectroscopic samples. Pushing to lower masses, we find that the fraction of passive satellites increases, potentially signalling a change in the dominant quenching mechanism at M⋆ ∼ 109 M⊙. Similar to the results of previous studies of the Local Group, this increase in the quenched fraction at low satellite masses may correspond to an increase in more » the efficacy of ram-pressure stripping as a quenching mechanism in groups. « less
Authors:
; ;
Award ID(s):
1815475
Publication Date:
NSF-PAR ID:
10326889
Journal Name:
Monthly Notices of the Royal Astronomical Society
Volume:
503
Issue:
2
Page Range or eLocation-ID:
1636 to 1645
ISSN:
0035-8711
Sponsoring Org:
National Science Foundation
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