arXiv:2608.27807·v1·High Energy Physics — Phenomenology
Future of Artificial Intelligence for Science in Japan 2024 Community Report
Yoshitaka Itow🇯🇵 · Jia Liu🇯🇵 · Hirokazu Maesaka🇯🇵 · Vinicius Mikuni🇯🇵 · Nhat-Minh Nguyen🇯🇵 · Hironao Miyatake🇯🇵 · Atsushi J. Nishizawa🇯🇵 · Patrick de Perio🇯🇵 · Daniel Ratner🇺🇸 · Kazuhiro Terao🇺🇸 · Leander Thiele · Omar Alterkait🇺🇸
Abstract
This white paper summarizes scientific challenges and AI/ML research opportunities identified through the FAIRS Japan 2024 unconference process. The discussion focuses on three major physics domains: accelerator physics, cosmology and astrophysics, and neutrino physics. Although each domain has distinct scientific goals and experimental constraints, several common technical themes emerge: high-dimensional reconstruction, fast and accurate simulation, uncertainty propagation, simulation-to-data mismatch, anomaly detection, real-time decision-making, and shared infrastructure.