Dynamical Mass Measurements of Contaminated Galaxy Clusters Using
  Machine Learning
  We study dynamical mass measurements of galaxy clusters contaminated by
interlopers and show that a modern machine learning (ML) algorithm can predict
masses by better than a factor of two compared to a standard scaling relation
approach. We create two mock catalogs from Multidark's publicly available
$N$-body MDPL1 simulation, one with perfect galaxy cluster membership
information and the other where a simple cylindrical cut around the cluster
center allows interlopers to contaminate the clusters. In the standard
approach, we use a power-law scaling relation to infer cluster mass from galaxy
line-of-sight (LOS) velocity dispersion. Assuming perfect membership knowledge,
this unrealistic case produces a wide fractional mass error distribution, with
a width of $\Delta\epsilon\approx0.87$. Interlopers introduce additional
scatter, significantly widening the error distribution further
($\Delta\epsilon\approx2.13$). We employ the support distribution machine (SDM)
class of algorithms to learn from distributions of data to predict single
values. Applied to distributions of galaxy observables such as LOS velocity and
projected distance from the cluster center, SDM yields better than a
factor-of-two improvement ($\Delta\epsilon\approx0.67$) for the contaminated
case. Remarkably, SDM applied to contaminated clusters is better able to
recover masses than even the scaling relation approach applied to
uncontaminated clusters. We show that the SDM method more accurately reproduces
the cluster mass function, making it a valuable tool for employing cluster
observations to evaluate cosmological models.
