Machine Learning for Multi-Messenger Probes of New Physics and Cosmology: Review and Perspective

The multi-messenger exploration of dark matter and physics beyond the Standard Model has emerged as a central direction in modern astro-particle physics, particularly following the discovery of gravitational waves. In this work, we present a comprehensive review and forward-looking perspective on machine-learning-enhanced multi-messenger approaches, combining information from gravitational waves, cosmic rays, gamma rays, neutrinos, and collider experiments. We summarize the current state of the field, discuss recent methodological developments, and outline a coherent research program aimed at integrating heterogeneous datasets within a unified inference framework. We collaboratively propose a plan for forthcoming analyses aiming at extracting information on the properties and interactions of dark matter, and finally on its genesis, combining multi-messenger astronomy techniques and inputs from laboratory physics. The main objectives planned in this line of research comprise: (i) the multi-messenger analysis of new physics in cosmology, including mainly, but not only, several different models of dark matter; (ii) the phenomenology of new physics signatures in ground-based cosmic rays experiments, with cross-correlation to the corresponding physical, astrophysical and cosmological observations; (iii) the development of machine learning methods for data analysis in ground-based cosmic rays experiments, in light of the new physics signatures. We note that several groups have explored the use of multi-messenger observations, including gravitational waves, to probe alternative dark matter candidates. The present work builds on these developments by focusing on the role of machine learning in integrating heterogeneous datasets. We foresee that a cross-fertilizing approach combining the information that arises from very different experimental methodologies will represent the right and successful path to extract information about the very elusive dark matter particles and provide answers to the main questions that are left in fundamental physics.

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Publication Details

Journal
Symmetry
Published
2026-06-30
DOI
https://doi.org/10.3390/sym18071116
Primary Topic
Dark Matter and Cosmic Phenomena
Type
article
Field-Weighted Citation Impact
0.00

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article

Machine Learning for Multi-Messenger Probes of New Physics and Cosmology: Review and Perspective

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Machine Learning for Multi-Messenger Probes of New Physics and Cosmology: Review and Perspective

V. Ya. Korchagin, Timur Bikbaev, S. Giagu, Oem Trivedi, Artem Kharakhashyan, De’en Chen, Antonino Marcianò, António Morais, Jackson Levi Said, Roman Pasechnik, Maxim Khlopov, A. G. Mayorov, Krid Jinklub, K. M. Belotsky, Vitaly Beylin, Danila Sopin, Atharv Mahajan, Viktor Stasenko, Filippo Fabrocini, Maxim Krasnov, Andrea Addazi
article en

Abstract

The multi-messenger exploration of dark matter and physics beyond the Standard Model has emerged as a central direction in modern astro-particle physics, particularly following the discovery of gravitational waves. In this work, we present a comprehensive review and forward-looking perspective on machine-learning-enhanced multi-messenger approaches, combining information from gravitational waves, cosmic rays, gamma rays, neutrinos, and collider experiments. We summarize the current state of the field, discuss recent methodological developments, and outline a coherent research program aimed at integrating heterogeneous datasets within a unified inference framework. We collaboratively propose a plan for forthcoming analyses aiming at extracting information on the properties and interactions of dark matter, and finally on its genesis, combining multi-messenger astronomy techniques and inputs from laboratory physics. The main objectives planned in this line of research comprise: (i) the multi-messenger analysis of new physics in cosmology, including mainly, but not only, several different models of dark matter; (ii) the phenomenology of new physics signatures in ground-based cosmic rays experiments, with cross-correlation to the corresponding physical, astrophysical and cosmological observations; (iii) the development of machine learning methods for data analysis in ground-based cosmic rays experiments, in light of the new physics signatures. We note that several groups have explored the use of multi-messenger observations, including gravitational waves, to probe alternative dark matter candidates. The present work builds on these developments by focusing on the role of machine learning in integrating heterogeneous datasets. We foresee that a cross-fertilizing approach combining the information that arises from very different experimental methodologies will represent the right and successful path to extract information about the very elusive dark matter particles and provide answers to the main questions that are left in fundamental physics.

SymmetryVol. 18(7)
Tongji University (CN), Southern Federal University (RU), Istituto Nazionale di Fisica Nucleare (IT), Lund University (SE), University of Malta (MT), Vanderbilt University (US), Sichuan University (CN), Fudan University (CN), LIP - Laboratory of Instrumentation and Experimental Particle Physics (PT), Laboratoire AstroParticule et Cosmologie (FR), Istituto Nazionale di Fisica Nucleare, Roma Tor Vergata (IT), Istituto Nazionale di Fisica Nucleare, Laboratori Nazionali di Frascati (IT), National Research Council (IT), Chiang Mai University (TH), Ahmedabad University (IN), Sapienza University of Rome (IT), National Research Nuclear University MEPhI (RU), University of Minho (PT)
Ministry of Science and Higher Education of the Russian Federation, Southern Federal University
Openalex Percentile: Top 10%
Dark Matter and Cosmic Phenomena
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