Machine-learning cosmological inferences from X-ray galaxy-cluster survey catalogs

Machine-learning cosmological inferences from X-ray galaxy-cluster survey catalogs | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Machine-learning cosmological inferences from X-ray galaxy-cluster survey catalogs Nicola Napolitano, Fucheng Zhong, Johan Comparat, Klaus Dolag, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8434390/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Galaxy clusters provide critical constraints on cosmological parameters; however, traditional approaches based on mass–observable scaling relations are subject to systematic uncertainties. Here, we present a machine-learning framework that directly infers cosmological parameters from real eROSITA galaxy cluster observations, bypassing explicit mass calibration. We train a random forest algorithm on Magneticum multi-cosmology hydrodynamical simulations and apply it to observed X-ray properties (gas luminosity, mass, and temperature) at different redshifts from the eROSITA catalogs. This method yields cosmological constraints including the matter density Ωm=0.30^+0.03_-0.02, and the 8 Mpc/h fluctuation amplitude, σ8=0.81^+0.01_-0.01, consistent with current state-of-the-art probes. Unlike other low-redshift cosmological datasets, these parameters show no significant tension with Cosmic Microwave Background inferences from the Planck mission. In contrast, our inference of the Hubble constant, h0=0.710^+0.004_-0.004, shows a significant deviation from the Planck value and lies slightly below most late–Universe determinations, while remaining comfortably consistent with the Tip of the Red Giant Branch (\textit{TRGB}) measurements. This suggests a potential reduction of the early–late Universe Hubble tension. This study represents the first direct cosmological inference from observational cluster data using machine learning trained on multi-cosmology simulations. It establishes an alternative route to precision cosmology using galaxy cluster observations via machine learning, and highlights the potential of large-scale X-ray surveys to deliver independent tests of the standard cosmological model. Physical sciences/Physics/Astronomy and astrophysics/Cosmology Physical sciences/Physics/Astronomy and astrophysics/Galaxies and clusters Physical sciences/Physics/Astronomy and astrophysics/Computational astrophysics Full Text Additional Declarations There is NO Competing Interest. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8434390","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":564719599,"identity":"029eae97-749c-4e4d-81ae-9a7e3bedc643","order_by":0,"name":"Nicola Napolitano","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABNklEQVRIie3RPUvDQBgH8CuBZIl0jSDJVzg5sAhKvsodQl2qCC4dMqQU0kXSNZtfoSAobgkPZGpxvaFIs2RyOChkDF7S+BIbnR3uzx083N3vXjiEVFT+YzTZN00dN0N+XfSbEbOL0Dbp+XFVHfoN2TPfCPog9VLc7PDzmIGhZRvqvdrObJXFAq2dgTGZgPDWNuFXCdx46Mhtk9OpTjBNbwleXuIkQvnx810iL5bm5IRfU4jSvYthMHWL6pQt0BCBiaC34EwSHdgjH1Wzv5CSsvt5XhN3R0pgD1FFym7CAsp8vjuF1SQJZGFJchB0EPkWFlKCeY6SCMNF/ZZVCMRavkkSWqYZt8kLZBtRUNuZDzUhxnD+ZExBjAuw+7MR2ZrFmWv4HR/ztUPdWrH+Wv+pVFRUVFRaeQf7P4AfUcqz8gAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0003-0911-8884","institution":"University of Naples Federico II","correspondingAuthor":true,"prefix":"","firstName":"Nicola","middleName":"","lastName":"Napolitano","suffix":""},{"id":564719600,"identity":"f939bfce-f930-4e68-8f29-47b2dbe076fc","order_by":1,"name":"Fucheng Zhong","email":"","orcid":"","institution":"Department of Physics E. 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Here, we present a machine-learning framework that directly infers cosmological parameters from real eROSITA galaxy cluster observations, bypassing explicit mass calibration. We train a random forest algorithm on Magneticum multi-cosmology hydrodynamical simulations and apply it to observed X-ray properties (gas luminosity, mass, and temperature) at different redshifts from the eROSITA catalogs. This method yields cosmological constraints including the matter density Ωm=0.30^+0.03_-0.02, and the 8 Mpc/h fluctuation amplitude, σ8=0.81^+0.01_-0.01, consistent with current state-of-the-art probes. Unlike other low-redshift cosmological datasets, these parameters show no significant tension with Cosmic Microwave Background inferences from the Planck mission. 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