Reconsidering the dynamical states of galaxy clusters using PCA and UMAP

TL;DR

This study employs PCA and UMAP to analyze multiple measures of galaxy cluster dynamical states, revealing their complex, multi-dimensional nature and challenging the traditional relaxed/unrelaxed classification.

Contribution

It introduces a multi-dimensional framework for understanding galaxy cluster dynamical states using PCA and UMAP, highlighting the limitations of single-scale classifications.

Findings

Four natural descriptions of dynamical state identified

Clusters can be relaxed according to some or none of these descriptions

Dynamical states are complex and multi-dimensional

Abstract

Numerous metrics exist to quantify the dynamical state of galaxy clusters, both observationally and within simulations. Many of these correlate strongly with one another, but it is not clear whether all of these measures probe the same intrinsic properties. In this work, we use two different statistical approaches -- principal component analysis (PCA) and uniform manifold approximation and projection (UMAP) -- to investigate which dynamical properties of a cluster are in fact the best descriptors of its dynamical state. We use measurements taken directly from The Three Hundred suite of galaxy cluster simulations, as well as morphological properties calculated using mock X-ray and SZ maps of the same simulated clusters. We find that four descriptions of dynamical state naturally arise, and although correlations exist between these, a given cluster can be "dynamically relaxed" according to all, none, or some of these four descriptions. These results demonstrate that it is highly important for future observational and theoretical studies to consider in which sense clusters are dynamically relaxed. Cluster dynamical states are complex and multi-dimensional, and so it is not meaningful to classify them simply as "relaxed" and "unrelaxed" based on a single linear scale.