DYNAMICAL MASS MEASUREMENTS OF CONTAMINATED GALAXY CLUSTERS USING MACHINE LEARNING
2016
We study dynamical mass measurements of galaxy clusters contaminated by interlopers and show that a modern
machine learning 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 D » 0.87.
Interlopers introduce additional scatter, significantly widening the error distribution further (D » 2.13). 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 (D » 0.67) 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.
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