From Observations to Simulations: A Neural-Network Approach to Intracluster Medium Kinematics

TL;DR

This paper uses deep learning to compare observed X-ray velocity maps of galaxy clusters with simulated data, revealing the physical processes shaping intracluster medium dynamics.

Contribution

It introduces a Siamese CNN approach to match observed and simulated cluster velocity maps, providing a novel data-driven method for studying ICM kinematics.

Findings

Simulated halos can replicate observed velocity gradients and substructures.

ICM motions are driven by gas sloshing, AGN feedback, and minor mergers.

Deep learning offers an objective way to connect observations with simulations.

Abstract

We present a systematic comparison between {\it XMM-Newton} velocity maps of the Virgo, Centaurus, Ophiuchus and A3266 clusters and synthetic velocity maps generated from the Illustris TNG-300 simulations. Our goal is to constrain the physical conditions and dynamical states of the intracluster medium (ICM) through a data-driven approach. We employ a Siamese Convolutional Neural Network (CNN) designed to identify the most analogous simulated cluster to each observed system based on the morphology of their line-of-sight velocity maps. The model learns a high-dimensional similarity metric between observations and simulations, allowing us to capture subtle kinematic and structural patterns beyond traditional statistical tests. We find that the best-matching simulated halos reproduce the observed large-scale velocity gradients and local kinematic substructures, suggesting that the ICM motions in these clusters arise from a combination of gas sloshing, AGN feedback, and minor merger activity. Our results demonstrate that deep learning provides a powerful and objective framework for connecting X-ray observations to cosmological simulations, offering new insights into the dynamical evolution of galaxy clusters and the mechanisms driving turbulence and bulk flows in the hot ICM.