2025/11/22 by Zhang, Z., Agapov, I., Gasiorowski, S. +4
Computer Science · Engineering · Physics and Astronomy · #Accelerator Physics (physics.acc-ph) #Advanced Multi-Objective Optimization Algorithms #FOS: Computer and information sciences #FOS: Physical sciences #Machine Learning (cs.LG) #Particle Accelerators and Free-Electron Lasers #Particle physics theoretical and experimental studies
paper · doi:10.48550/arxiv.2511.17850
openalex publication_date 2025/11/22 · openalex created_date 2025/11/27 · openalex updated_date 2026/07/28
We demonstrate that multipoint Bayesian algorithm execution can overcome fundamental computational challenges in storage ring design optimization. Dynamic (DA) and momentum (MA) optimization is a multipoint, multiobjective design task for storage rings, ultimately informing the flux of x-ray sources and luminosity of colliders. Current state-of-art black-box optimization methods require extensive particle-tracking simulations for each trial configuration; the high computational cost restricts the extent of the search to ∼ 103 configurations, and therefore limits the quality of the final design. We remove this bottleneck using multipointBAX, which selects, simulates, and models each trial configuration at the single particle level. We demonstrate our approach on a novel design for a fourth-generation light source, with neural-network powered multipointBAX achieving equivalent Pareto front results using more than two orders of magnitude fewer tracking computations compared to genetic algorithms. The significant reduction in cost positions multipointBAX as a promising alternative to black-box optimization, and we anticipate multipointBAX will be instrumental in the design of future light sources, colliders, and large-scale scientific facilities.