The Dynamical Mass of the Coma Cluster from Deep Learning

TL;DR

This paper introduces a novel deep learning approach to estimate the mass of the Coma galaxy cluster, providing more precise and statistically consistent measurements that enhance our understanding of dark matter distribution in such systems.

Contribution

The paper develops a Bayesian deep learning method to accurately estimate the dynamical mass of the Coma cluster, demonstrating its robustness and consistency with previous estimates.

Findings

Estimated Coma's mass as 10^15.10 ± 0.15 solar masses.

Predicted mass within a radius of 1.78 ± 0.03 h^{-1} Mpc.

Method is statistically consistent with historical measurements.

Abstract

In 1933, Fritz Zwicky's famous investigations of the mass of the Coma cluster led him to infer the existence of dark matter \cite{1933AcHPh...6..110Z}. His fundamental discoveries have proven to be foundational to modern cosmology; as we now know such dark matter makes up 85\% of the matter and 25\% of the mass-energy content in the universe. Galaxy clusters like Coma are massive, complex systems of dark matter in addition to hot ionized gas and thousands of galaxies, and serve as excellent probes of the dark matter distribution. However, empirical studies show that the total mass of such systems remains elusive and difficult to precisely constrain. Here, we present new estimates for the dynamical mass of the Coma cluster based on Bayesian deep learning methodologies developed in recent years. Using our novel data-driven approach, we predict Coma's $\mthc$ mass to be $10^{15.10 \pm 0.15}\ \hmsun$ within a radius of $1.78 \pm 0.03\ h^{-1}\mathrm{Mpc}$ of its center. We show that our predictions are rigorous across multiple training datasets and statistically consistent with historical estimates of Coma's mass. This measurement reinforces our understanding of the dynamical state of the Coma cluster and advances rigorous analyses and verification methods for empirical applications of machine learning in astronomy.