A machine learning approach to measuring the quenched fraction of low-mass satellites beyond the Local Group

Observations suggest that satellite quenching plays a major role in the build-up of passive, low-mass galaxies at late cosmic times. Studies satellites, however, are limited by ability to robustly characterize local environment and star-formation activity faint systems. In an effort overcome limitations existing data sets, we utilize deep photometry Stripe 82 Sloan Digital Sky Survey, conjunction with neural network classification scheme, study suppression star formation Universe. Using statistically-driven approach, able push beyond limits spectroscopic measuring quenched fraction down stellar masses ${\sim}10^7~{\rm M}_{\odot}$ group environments (${M}_{\rm{halo}} = 10^{13-14}~h^{-1}~{\rm M}_{\odot}$). At high ($\gtrsim 10^{10}~{\rm M}_{\odot}$), our analysis successfully reproduces measurements based on samples. Pushing lower masses, find passive satellites increases, potentially signaling change dominant mechanism ${M}_{\star} \sim 10^{9}~{\rm M}_{\odot}$. Similar results previous studies Local Group, this increase low may correspond efficacy ram-pressure stripping as groups.