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

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.