| Issue |
A&A
Volume 710, June 2026
|
|
|---|---|---|
| Article Number | A54 | |
| Number of page(s) | 11 | |
| Section | Cosmology (including clusters of galaxies) | |
| DOI | https://doi.org/10.1051/0004-6361/202558245 | |
| Published online | 28 May 2026 | |
From observations to simulations: A neural-network approach to intracluster medium kinematics
1
Max-Planck-Institut für Extraterrestrische Physik, Gießenbachstraße 1, 85748, Garching, Germany
2
Harvard-Smithsonian Center for Astrophysics, 60 Garden Street, Cambridge, MA, 02138, USA
3
Institute of Astronomy, Madingley Road, Cambridge, CB3 0HA, UK
4
Institute for Frontiers in Astronomy and Astrophysics, Beijing Normal University, Beijing, 102206, China
5
INAF – IASF Palermo, Via U. La Malfa 153, I-90146, Palermo, Italy
6
Department of Physics and Astronomy, University of Alabama in Huntsville, Huntsville, AL, 35899, USA
★ Corresponding author: This email address is being protected from spambots. You need JavaScript enabled to view it.
Received:
24
November
2025
Accepted:
25
April
2026
Abstract
We present a systematic comparison between XMM-Newton velocity maps of the Virgo, Centaurus, Ophiuchus, and A3266 clusters and synthetic velocity maps generated from the Illustris TNG-300 simulated clusters. Our goal is to constrain the physical conditions and dynamical states of the intracluster medium (ICM) through a data-driven approach. We employed 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 simulated clusters, 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, active galactic nucleus 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.
Key words: galaxies: clusters: intracluster medium / galaxies: clusters: individual: ophiuchus / galaxies: clusters: individual: centaurus / galaxies: clusters: individual: virgo / galaxies: clusters: individual: A3266 / X-rays: galaxies: clusters
© The Authors 2026
Open Access article, published by EDP Sciences, under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Open access funding provided by Max Planck Society.
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