A Machine Learning Approach for Dynamical Mass Measurements of Galaxy Clusters

Understand

We present a modern machine learning approach for cluster dynamical mass measurements that is a factor of two improvement over using a conventional scaling relation.

• Different methods are tested against a mock cluster catalog constructed using halos with mass >= 10^14 Msolar/h from Multidark's publicly-available N-body MDPL halo catalog.
• In the conventional method, we use a standard M(sigma_v) power law scaling relation to infer cluster mass, M, from line-of-sight (LOS) galaxy velocity dispersion, sigma_v.
• The resulting fractional mass error distribution is broad, with width=0.87 (68% scatter), and has extended high-error tails.