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

Sascha Caron, Andreas Ipp, Gert Aarts, Gábor Bíró, Daniele Bonacorsi, Elena Cuoco, Caterina Doglioni, Tommaso Dorigo, Julián García Pardiñas, Stefano Giagu, Tobias Golling, Lukas Heinrich, Ik Siong Heng, Paula Gina Isar, Karolos Potamianos, Liliana Teodorescu, John Veitch, Pietro Vischia, Christoph Weniger

TL;DR

This paper addresses the scaling of AI infrastructure for fundamental physics within the JENA communities and EuCAIF. It collects input from 137 respondents (July–November 2024) and translates findings into 12 concrete recommendations spanning HPC, data, production, LLMs, foundation models, benchmarks, energy efficiency, FAIR, training, and interdisciplinarity. It foregrounds a strategic choice between a centralized large-scale GPU facility and federated/hybrid HPC and outlines funding schemes and an organizational structure to coordinate AI investments. The work aims to accelerate AI-enabled discovery in particle, nuclear, and astroparticle physics and to strengthen Europe’s capability to deploy AI across next-generation colliders and experiments.

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 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.

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

TL;DR

This paper addresses the scaling of AI infrastructure for fundamental physics within the JENA communities and EuCAIF. It collects input from 137 respondents (July–November 2024) and translates findings into 12 concrete recommendations spanning HPC, data, production, LLMs, foundation models, benchmarks, energy efficiency, FAIR, training, and interdisciplinarity. It foregrounds a strategic choice between a centralized large-scale GPU facility and federated/hybrid HPC and outlines funding schemes and an organizational structure to coordinate AI investments. The work aims to accelerate AI-enabled discovery in particle, nuclear, and astroparticle physics and to strengthen Europe’s capability to deploy AI across next-generation colliders and experiments.

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 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.

Paper Structure

This paper contains 18 sections, 5 figures.

Table of Contents

  1. Introduction
  2. Survey: Community Insights
  3. Overview of the Survey
  4. Key Findings
  5. Interpretation of Results
  6. Key Messages and Recommendations
  7. (R1) Scalability and Access to HPC Resources
  8. (R2) Data Infrastructure and Infrastructure for Distribution
  9. (R3) Integrating AI from R&D into Production-ready Applications
  10. (R4) Advancing Machine Learning for Production Workflows
  11. (R5) Large Language Models and Fundamental Physics
  12. (R6) Foundation Models and Large-scale Machine Learning
  13. (R7) Benchmarking and Standards
  14. (R8) Energy Efficiency
  15. (R9) FAIR Principles
  16. ...and 3 more sections

Figures (5)

  • Figure 1: Response to the question "[2/40] What is your main scientific field (or JENA community)?"
  • Figure 2: Response to the question "[5/40] What is your usage of AI? Which ML techniques do you use?"
  • Figure 3: Response to the questions "[20/40] Did you ever manage to reproduce someone’s paper results / retrain their model?" (left panel) and "[30/40] Should we collaborate more in the development of large-scale ML models (e.g. foundation models) for physics?" (right panel).
  • Figure 4: Response to the questions "[9/40] In your current work, what is the predominant status of your machine learning implementations?" (left panel) and "[34/40] How do you foresee the status of ML implementations evolving in your field over the next five years?" (right panel).
  • Figure 5: Response to the question "[26/40] How important would high-end/large GPUs (like the A100 etc) be for training large-scale deep learning models over the next five years?"'