Strategic white paper on AI infrastructure for particle, nuclear, and astroparticle physics: insights from JENA and EuCAIF

Abstract Artificial intelligence (AI) is transforming scientific research, with deep learning methods playing a central role in data analysis, simulations, and signal detection across particle, nuclear, and astroparticle physics. Within the JENA (Joint ECFA, NuPECC, APPEC) communities and as part of the European Coalition for AI in Fundamental Physics initiative, AI integration is advancing steadily. However, broader adoption remains constrained by challenges such as limited computational resources, a lack of expertise, and difficulties in transitioning from research and development to production. This white paper provides a strategic roadmap, informed by a community survey, to address these barriers. It outlines critical infrastructure requirements, prioritises training initiatives, and proposes funding strategies to scale AI capabilities across fundamental physics over the next five years.

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Publication Details

Journal
Machine Learning Science and Technology
Published
2026-01-08
DOI
https://doi.org/10.1088/2632-2153/ae35cd
Primary Topic
International Science and Diplomacy
Type
article
Field-Weighted Citation Impact
0.00
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article

Strategic white paper on AI infrastructure for particle, nuclear, and astroparticle physics: insights from JENA and EuCAIF

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Machine Learning Science and Technology
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article

Strategic white paper on AI infrastructure for particle, nuclear, and astroparticle physics: insights from JENA and EuCAIF

Andreas Ipp, Julian Garcia Pardinas, Gert Aarts, Karolos Potamianos, Tommaso Dorigo, Tobias Golling, Caterina Doglioni, John Veitch, Lukas Heinrich, Paula Gina Isar, Liliana Teodorescu, Gabor Biro, Daniele Bonacorsi, Elena Cuoco, Stefano Giagu, Sascha Caron
article en

Abstract

Abstract Artificial intelligence (AI) is transforming scientific research, with deep learning methods playing a central role in data analysis, simulations, and signal detection across particle, nuclear, and astroparticle physics. Within the JENA (Joint ECFA, NuPECC, APPEC) communities and as part of the European Coalition for AI in Fundamental Physics initiative, AI integration is advancing steadily. However, broader adoption remains constrained by challenges such as limited computational resources, a lack of expertise, and difficulties in transitioning from research and development to production. This white paper provides a strategic roadmap, informed by a community survey, to address these barriers. It outlines critical infrastructure requirements, prioritises training initiatives, and proposes funding strategies to scale AI capabilities across fundamental physics over the next five years.

Machine Learning Science and TechnologyVol. 7(1)
Eötvös Loránd University (HU), University of Geneva (CH), TU Wien (AT), Radboud University Nijmegen (NL), Universidad de Oviedo (ES), Luleå University of Technology (SE), University of Manchester (GB), University of Warwick (GB), Swansea University (GB), National Institute for Subatomic Physics (NL), Istituto Nazionale di Fisica Nucleare, Sezione di Padova (IT), HUN-REN Wigner Research Centre for Physics (HU), Institute of Space Science - INFLPR Subsidiary (RO), Istituto Nazionale di Fisica Nucleare, Sezione di Bologna (IT), Istituto Nazionale di Fisica Nucleare, Sezione di Roma I (IT), Brunel University of London (GB), Technical University of Munich (DE), Massachusetts Institute of Technology (US), University of Glasgow (GB), Sapienza University of Rome (IT), University of Amsterdam (NL), University of Bologna (IT)
Industry, innovation and infrastructure
Openalex Percentile: Top 94%
International Science and Diplomacy
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