Emulating Sunyaev-Zeldovich Images of Galaxy Clusters using Auto-Encoders

TL;DR

This paper presents a machine learning approach using a conditional variational autoencoder to generate high-resolution Sunyaev-Zeldovich maps of galaxy clusters efficiently, capturing complex structures and dynamics from simulation data.

Contribution

The authors develop a CVAE-based method that produces realistic SZ maps from halo properties, outperforming analytical models in detail and computational speed.

Findings

Accurately reproduces internal cluster structures and asymmetries.

Generates over 100,000 SZ maps in 30 seconds on a laptop.

Captures effects of mass and accretion rate on SZ images.

Abstract

We develop a machine learning algorithm that generates high-resolution thermal Sunyaev-Zeldovich (SZ) maps of novel galaxy clusters given only halo mass and mass accretion rate. The algorithm uses a conditional variational autoencoder (CVAE) in the form of a convolutional neural network and is trained with SZ maps generated from the IllustrisTNG simulation. Our method can reproduce many of the details of galaxy clusters that analytical models usually lack, such as internal structure and aspherical distribution of gas created by mergers, while achieving the same computational feasibility, allowing us to generate mock SZ maps for over $10^5$ clusters in 30 seconds on a laptop. We show that the model is capable of generating novel clusters (i.e. not found in the training set) and that the model accurately reproduces the effects of mass and mass accretion rate on the SZ images, such as scatter, asymmetry, and concentration, in addition to modeling merging sub-clusters. This work demonstrates the viability of machine-learning--based methods for producing the number of realistic, high-resolution maps of galaxy clusters necessary to achieve statistical constraints from future SZ surveys.