The AI/ML for particle accelerators living review

LIVING REVIEW • CONTINUOUSLY UPDATED 🕒 Last update: October 19, 2026, 09:22 UTC 🔮 Next update: October 20, 2026, 09:22 UTC
267+
Papers Reviewed
15
Categories
Oct 2026
Last Updated
6
Papers This Month

Overview

This living review provides a comprehensive survey of artificial intelligence and machine learning applications in particle accelerator science. As the field rapidly evolves, we continuously update this review to reflect the latest research, methodologies, and experimental results from facilities worldwide. Our goal is to serve as a central reference for researchers, operators, and engineers working at the intersection of AI/ML and accelerator physics.

Latest Additions

Recently added papers from October 2026

Reveal normal form structure for nonlinear map in accelerator beam physics with symplectic neural network
Leo He, Yue Hao • arXiv (2026)
Learn the Dynamics, Enforce the Physics: Generative Latent Operators for High-Dimensional Beam Evolution
Haohan Zou, Alexander Scheinker, Yuanyuan Shi • arXiv (2026)
Multi-Objective Design of C-Shaped Targets for Energy--Divergence Control in Laser-Driven Proton Acceleration
Mohammad Rezaei-Pandari, Mahdi Shayganmanesh, Mohammad Hossein Mahdieh • arXiv (2026)
Anomaly Detection in Bunch Oscillations for Early Warning of Sudden Beam Loss at SuperKEKB
H. Haigh, P. Horak, G. Inguglia, et al. • arXiv (2026)
Orbit correction for a rapid cycling synchrotron using an adjacent-difference-constrained neural network
Zhong, Hao, Lu, Xiaohan, Bao, Wei, et al. • Phys.Rev.Accel.Beams (2026)
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