A Generative Model for Realistic Galaxy Cluster X-ray Morphologies

TL;DR

This paper introduces a generative model for realistic galaxy cluster X-ray images using principal component analysis on simulated data, enabling the creation of complex, customizable cluster morphologies for analysis and training purposes.

Contribution

It presents a novel PCA-based generative approach to produce realistic, complex galaxy cluster X-ray images, facilitating improved analysis and algorithm training.

Findings

Generated realistic cluster images with diverse features

Provided code for image generation and customization

Enhanced data for cluster morphology analysis

Abstract

The X-ray morphologies of clusters of galaxies display significant variations, reflecting their dynamical histories and the nonlinear dependence of X-ray emissivity on the density of the intracluster gas. Qualitative and quantitative assessments of X-ray morphology have long been considered a proxy for determining whether clusters are dynamically active or "relaxed." Conversely, the use of circularly or elliptically symmetric models for cluster emission can be complicated by the variety of complex features realized in nature, spanning scales from Mpc down to the resolution limit of current X-ray observatories. In this work, we use mock X-ray images from simulated clusters from THE THREE HUNDRED project to define a basis set of cluster image features. We take advantage of clusters' approximate self similarity to minimize the differences between images before encoding the remaining diversity through a distribution of high order polynomial coefficients. Principal component analysis then provides an orthogonal basis for this distribution, corresponding to natural perturbations from an average model. This representation allows novel, realistically complex X-ray cluster images to be easily generated, and we provide code to do so. The approach provides a simple way to generate training data for cluster image analysis algorithms, and could be straightforwardly adapted to generate clusters displaying specific types of features, or selected by physical characteristics available in the original simulations.