Machine Learning applications to Galaxy Clusters

TL;DR

This paper reviews how AI techniques are applied to galaxy cluster studies, enhancing mass estimation, understanding cluster properties, and leveraging simulations for future cosmological surveys.

Contribution

It provides a comprehensive overview of recent AI advancements in galaxy cluster research, emphasizing simulation use, uncertainty quantification, and future prospects.

Findings

AI captures non-linear features and complex morphologies in cluster data.

AI improves mass estimation from various observational tracers.

Simulations play a crucial role in training AI models for galaxy cluster analysis.

Abstract

This chapter reviews the application of Artificial Intelligence (AI) techniques to the study of galaxy clusters, covering both theoretical developments and their use as tools to infer cluster properties from a variety of observational tracers. We discuss recent advances in mass estimation from SZ, X-ray, optical, and dynamical data, highlighting the ability of AI methods to capture non-linear features, projection effects, and complex cluster morphologies beyond more classical approaches. In addition, we present other emerging applications, including the emulation of baryonic physics from N-body simulations, the characterization of dynamical states and mergers, and the analysis of the diffuse components such as the intracluster light. Particular emphasis is placed on the role of simulations in training these models, the impact of baryonic modelling, and the need for a robust uncertainty quantification and interpretability. Finally, we outline current limitations and future prospects, stressing the importance of combining flexible simulation strategies with AI techniques to fully exploit next-generation surveys for precision cosmology.