Classification of Galaxy Cluster Membership with Machine Learning
  We present a classification algorithm based on the Random Forest machine
learning model that differentiates galaxies into orbiting, infalling and
background (interloper) populations, using phase space information as input. We
train and test our model with the galaxies from UniverseMachine mock catalog
based on Multi-Dark Planck 2 N-body simulations. We show that we can recover
the distribution of orbiting and infalling galaxies in 3D and 2D position and
velocity distribution with $<1\%$ error when using probabilistic predictions in
the presence of interlopers in the projected phase space. Our machine
learning-based galaxy classification method enables a percent level
measurements of the dynamics of cluster galaxies. We discuss potential
applications of this technique to improve cluster cosmology and galaxy
quenching.
