A Machine Learning Approach for Dynamical Mass Measurements of Galaxy Clusters

TL;DR

This paper introduces a machine learning method using Support Distribution Machines to improve galaxy cluster mass measurements from velocity data, significantly reducing errors compared to traditional scaling relations.

Contribution

The study demonstrates that machine learning, specifically SDMs, outperforms conventional methods in dynamical mass estimation of galaxy clusters, eliminating error tails and reducing scatter.

Findings

Support Distribution Machines reduce mass error width by 47%.

Including higher-order moments improves traditional scaling relation accuracy.

Machine learning eliminates problematic high-error tails in mass estimates.

Abstract

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. The standard scaling relation can be simply enhanced by including higher-order moments of the LOS velocity distribution. Applying the kurtosis as a correction term to log(sigma_v) reduces the width of the error distribution to 0.74 (16% improvement). Machine learning can be used to take full advantage of all the information in the velocity distribution. We employ the Support Distribution Machines (SDMs) algorithm that learns from distributions of data to predict single values. SDMs trained and tested on the distribution of LOS velocities yield width=0.46 (47% improvement). Furthermore, the problematic tails of the mass error distribution are effectively eliminated. Decreasing cluster mass errors will improve measurements of the growth of structure and lead to tighter constraints on cosmological parameters.