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

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.

💡 Research Summary

The white paper “Strategic White Paper on AI Infrastructure for Particle, Nuclear, and Astroparticle Physics: Insights from JENA and EuCAIF” presents a comprehensive roadmap for scaling artificial‑intelligence (AI) capabilities across the European fundamental‑physics community. Drawing on a detailed survey of 137 researchers from the JENA umbrella (ECFA, NuPECC, APPEC) and the EuCAIF initiative, the authors identify the current state of AI adoption, the most pressing bottlenecks, and a set of twelve concrete recommendations (R1‑R12) to be implemented over the next five years.

Context and Motivation
AI, especially deep learning (DL) and large language models (LLMs), is already reshaping data analysis, simulation, and signal detection in high‑energy physics (HEP). Experiments such as ATLAS and CMS have integrated AI into production workflows, but nuclear and astroparticle experiments still rely mainly on R&D‑level prototypes. The community recognizes AI’s transformative potential but faces systemic obstacles that prevent widespread, production‑grade deployment.

Survey Findings
The questionnaire covered 40 items on AI tools, computational resources, data types, and future needs. Key statistics include:

• 74 % of respondents regularly use commercial LLMs (e.g., ChatGPT); 26 % have tried open‑source alternatives.
• 71 % favour PyTorch, 44 % TensorFlow, and 49 % Scikit‑learn for model development.
• Workloads are distributed evenly across personal laptops/desktops, local computing farms, and large HPC clusters (≈55 % overall).
• Typical training datasets range from 10 GB to 1 TB; GPU memory requirements span 16 GB–100 GB, with only 5 % needing >100 GB.
• 48.5 % have successfully reproduced another group’s results, while the main failure causes are insufficient compute resources and lack of data access.

These results underline a community that is technically savvy yet constrained by hardware, software standardisation, and reproducibility infrastructure.

Strategic Recommendations (R1‑R12)

1. R1 – Scalability and Access to HPC Resources – Form dedicated working groups to evaluate a centralized large‑scale GPU facility versus a federated hybrid HPC model, producing detailed implementation plans for both.

2. R2 – Data Infrastructure and Distribution – Launch a community‑driven initiative to create shared repositories, metadata standards, and distributed workload platforms, backed by targeted funding.

3. R3 – From R&D to Production‑Ready Applications – Provide funding mechanisms that incentivise the integration of AI prototypes into existing experimental pipelines, emphasizing best‑practice software engineering and minimal disruption.

4. R4 – Machine‑Learning Operations (MLOps) – Allocate resources for dedicated MLOps personnel, develop community‑wide tools for CI/CD, model versioning, monitoring, and lifecycle management.

5. R5 – Science‑Specific Large Language Models – Invest in the development of domain‑tailored LLMs that embed physics terminology, constraints, and citation practices, while maintaining access to commercial models for generic tasks.

6. R6 – Foundation Models for Fundamental Physics – Fund the creation of large‑scale foundation models trained on curated physics datasets (both synthetic and real), with benchmarks that address domain shift, explainability, and physics‑informed augmentations.

7. R7 – Benchmarking and Standards – Establish a sustainable effort to maintain extensible benchmarks for classification, inference, tracking, and anomaly detection, encouraging open‑source surrogate models and reproducibility.

8. R8 – Energy Efficiency – Develop and adopt metrics that quantify the carbon footprint of AI training and inference; promote optimisation of frameworks and hardware to minimise energy consumption.

9. R9 – FAIR Principles Integration – Embed FAIR compliance into publication criteria, funding calls, and career evaluation, and support the creation of tooling that facilitates FAIR‑aligned data and model sharing.

10. R10 – Training and Education – Create practical courses, summer schools, and industry‑partnered internships that teach reproducible AI research, open‑source tooling, and real‑world project experience.

11. R11 – Interdisciplinary Collaboration – Set up interdisciplinary consortia that bring together physicists, AI researchers, software engineers, and HPC experts, with shared repositories and joint workshops to foster cross‑domain knowledge transfer.

12. R12 – Coordinated Governance – Establish a dedicated organisational structure (e.g., a European AI‑for‑Fundamental‑Physics board) to coordinate investments, monitor progress, and align national funding with the EuCAIF model.

Impact Assessment
The authors provide quantitative expectations: MLOps staffing (R4) could cut model deployment time by ~70 % and reduce reproducibility failures by 30 %; domain‑specific LLMs (R5) and foundation models (R6) are projected to improve physics‑task accuracy by at least 15 % over generic models. Energy‑efficiency measures (R8) aim to lower AI‑related carbon emissions by 20 % across participating institutions.

Conclusion
By addressing hardware scarcity, software standardisation, expertise gaps, and sustainability concerns in a coordinated, funded, and community‑driven manner, the roadmap promises to transform AI from an experimental add‑on into a core pillar of particle, nuclear, and astroparticle research. The paper calls for immediate action from funding agencies, HPC centres, and scientific societies to realise a European AI ecosystem that accelerates discovery, nurtures talent, and aligns with open‑science principles.