Strategic White Paper on AI Infrastructure for Particle, Nuclear, and Astroparticle Physics: Insights from JENA and EuCAIF
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| Main Authors: | , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912898221080576 |
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| author | Caron, Sascha Ipp, Andreas Aarts, Gert Bíró, Gábor Bonacorsi, Daniele Cuoco, Elena Doglioni, Caterina Dorigo, Tommaso Pardiñas, Julián García Giagu, Stefano Golling, Tobias Heinrich, Lukas Heng, Ik Siong Isar, Paula Gina Potamianos, Karolos Teodorescu, Liliana Veitch, John Vischia, Pietro Weniger, Christoph |
| author_facet | Caron, Sascha Ipp, Andreas Aarts, Gert Bíró, Gábor Bonacorsi, Daniele Cuoco, Elena Doglioni, Caterina Dorigo, Tommaso Pardiñas, Julián García Giagu, Stefano Golling, Tobias Heinrich, Lukas Heng, Ik Siong Isar, Paula Gina Potamianos, Karolos Teodorescu, Liliana Veitch, John Vischia, Pietro Weniger, Christoph |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_14192 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Strategic White Paper on AI Infrastructure for Particle, Nuclear, and Astroparticle Physics: Insights from JENA and EuCAIF Caron, Sascha Ipp, Andreas Aarts, Gert Bíró, Gábor Bonacorsi, Daniele Cuoco, Elena Doglioni, Caterina Dorigo, Tommaso Pardiñas, Julián García Giagu, Stefano Golling, Tobias Heinrich, Lukas Heng, Ik Siong Isar, Paula Gina Potamianos, Karolos Teodorescu, Liliana Veitch, John Vischia, Pietro Weniger, Christoph Instrumentation and Methods for Astrophysics High Energy Astrophysical Phenomena Artificial Intelligence Machine Learning High Energy Physics - Experiment High Energy Physics - Phenomenology Nuclear Theory 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. |
| title | Strategic White Paper on AI Infrastructure for Particle, Nuclear, and Astroparticle Physics: Insights from JENA and EuCAIF |
| topic | Instrumentation and Methods for Astrophysics High Energy Astrophysical Phenomena Artificial Intelligence Machine Learning High Energy Physics - Experiment High Energy Physics - Phenomenology Nuclear Theory |
| url | https://arxiv.org/abs/2503.14192 |