# Tuning Particle Accelerators with Safety Constraints using Bayesian Optimization

arXiv: https://arxiv.org/abs/2203.13968 · HTML: https://mlsanity.com/paper/2203.13968/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 59 references that are in the map.
- First posted on arXiv 26 Mar 2022.

## Builds on (most cited first, 13 of 13)

- [A Tutorial on Bayesian Optimization](https://mlsanity.com/paper/1807.02811/) (2018)
- [On Kernelized Multi-armed Bandits](https://mlsanity.com/paper/1704.00445/) (2017)
- [Gaussian Processes and Kernel Methods: A Review on Connections and Equivalences](https://mlsanity.com/paper/1807.02582/) (2018)
- [Bayesian Optimization with Safety Constraints: Safe and Automatic Parameter Tuning in Robotics](https://mlsanity.com/paper/1602.04450/) (2016)
- [Scalable Constrained Bayesian Optimization](https://mlsanity.com/paper/2002.08526/) (2020)
- [Adaptive and Safe Bayesian Optimization in High Dimensions via One-Dimensional Subspaces](https://mlsanity.com/paper/1902.03229/) (2019)
- [No-Regret Bayesian Optimization with Unknown Hyperparameters](https://mlsanity.com/paper/1901.03357/) (2019)
- [A General Framework for Constrained Bayesian Optimization using Information-based Search](https://mlsanity.com/paper/1511.09422/) (2015)
- [Unimodal Bandits: Regret Lower Bounds and Optimal Algorithms](https://mlsanity.com/paper/1405.5096/) (2014)
- [Online Primal-Dual Mirror Descent under Stochastic Constraints](https://mlsanity.com/paper/1908.00305/) (2019)
- [Versatile Black-Box Optimization](https://mlsanity.com/paper/2004.14014/) (2020)
- [Physics-informed Gaussian Process for Online Optimization of Particle Accelerators](https://mlsanity.com/paper/2009.03566/) (2020)
- [Forced-exploration free Strategies for Unimodal Bandits](https://mlsanity.com/paper/2006.16569/) (2020)

## Built on by (most cited first, 7 of 7)

- [Minimizing UCB: a Better Local Search Strategy in Local Bayesian Optimization](https://mlsanity.com/paper/2405.15285/) (2024)
- [Transition Constrained Bayesian Optimization via Markov Decision Processes](https://mlsanity.com/paper/2402.08406/) (2024)
- [Sample-efficient Bayesian Optimisation Using Known Invariances](https://mlsanity.com/paper/2410.16972/) (2024)
- [Classifier-pruned Bayesian optimization for particle accelerator tuning: Exploring temporally structured manifold of 6D beam phase space](https://mlsanity.com/paper/2412.01748/) (2024)
- [Forecasting Particle Accelerator Interruptions Using Logistic LASSO Regression](https://mlsanity.com/paper/2303.08984/) (2023)
- [Safe Bayesian Optimization for Complex Control Systems via Additive Gaussian Processes](https://mlsanity.com/paper/2408.16307/) (2024)
- [Advancing accelerator virtual beam diagnostics through latent evolution modeling: an integrated solution to forward, inverse, tuning, and UQ problems](https://mlsanity.com/paper/2602.22618/) (2026)

## Abstract (arXiv)

Tuning machine parameters of particle accelerators is a repetitive and time-consuming task that is challenging to automate. While many off-the-shelf optimization algorithms are available, in practice their use is limited because most methods do not account for safety-critical constraints in each iteration, such as loss signals or step-size limitations. One notable exception is safe Bayesian optimization, which is a data-driven tuning approach for global optimization with noisy feedback. We propose and evaluate a step-size limited variant of safe Bayesian optimization on two research facilities of the Paul Scherrer Institut (PSI): a) the Swiss Free Electron Laser (SwissFEL) and b) the High-Intensity Proton Accelerator (HIPA). We report promising experimental results on both machines, tuning up to 16 parameters subject to 224 constraints.