A Machine Learning Approach to Measuring the Quenched Fraction of Low-Mass Satellites Beyond the Local Group
Devontae C. Baxter, M. C. Cooper, Sean P. Fillingham

TL;DR
This study employs deep photometry and neural networks to measure the quenched fraction of low-mass satellite galaxies beyond the Local Group, revealing an increase in passive satellites at lower masses and insights into quenching mechanisms.
Contribution
It introduces a novel neural network-based method combined with deep photometry to extend satellite quenching measurements to lower stellar masses beyond spectroscopic data limits.
Findings
Quenched fraction increases for satellites below 10^9 solar masses.
Method reproduces known results at higher masses.
Suggests ram-pressure stripping becomes more effective at low masses.
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 in group environments (). At high…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
