The AI/ML for particle accelerators living review

LIVING REVIEW • CONTINUOUSLY UPDATED 🕒 Last update: August 28, 2026, 15:28 UTC 🔮 Next update: August 29, 2026, 15:28 UTC
240+
Papers Reviewed
15
Categories
Aug 2026
Last Updated
14
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 August 2026

Machine Learning Optimization of E-Beam Transport for a Superradiant FEL
Amir Weinberg, Leon Feigin, Ariel Nause, et al. • arXiv (Cornell University) (2026)
Contrastive Learning for Interpretable Anomaly Detection at Collider Experiments
Haoyi Jia, Sagar Addepalli, Julia Gonski • arXiv (2026)
Particle tracking with physics-informed deep learning methods
Matthias Remta, Anja Beck, Shanthalakshmi Kilambi, et al. • arXiv (Cornell University) (2026)
Relative and absolute dosimetric commissioning of the ELIMAIA--ELIMED laser-driven proton beamline at 23.45 MeV
G. A. P. Cirrone, G. Petringa, A. Kurmanova, et al. • arXiv (2026)
Bayesian Optimization of Molybdenum-99 Production by Laser Wakefield Acceleration Using Coupled PIC and Monte Carlo Simulations
Bruno Silveira Nunes, Nilson Dias Vieira Junior, Mirko Salomón Alva Sánchez, et al. • arXiv (2026)
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