Strategic White Paper on AI Infrastructure for Particle, Nuclear, and Astroparticle Physics: Insights from JENA and EuCAIF

arXiv:2503.14192v13 citationsh-index: 119Machine Learning: Science and Technology
Originality Synthesis-oriented
AI Analysis

It tackles infrastructure and adoption challenges for researchers in fundamental physics, but is incremental as it focuses on strategic planning rather than new scientific results.

This white paper addresses the barriers to broader AI adoption in particle, nuclear, and astroparticle physics, such as limited computational resources and expertise, by providing a strategic roadmap with infrastructure requirements, training initiatives, and funding strategies for scaling AI capabilities over five years.

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 communities-ECFA, NuPECC, and APPEC-and as part of the EuCAIF 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 (R&D) to production. This white paper provides a strategic roadmap, informed by a community survey, to address these barriers. It outlines critical infrastructure requirements, prioritizes training initiatives, and proposes funding strategies to scale AI capabilities across fundamental physics over the next five years.

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