Leveraging prior mean models for faster Bayesian optimization of particle accelerators

Tuning particle accelerators is a challenging and time-consuming task that can be automated and carried out efficiently using suitable optimization algorithms, such as model-based Bayesian optimization techniques. One of the major advantages of Bayesian algorithms is the ability to incorporate prior...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	Scientific reports Ročník 15; číslo 1; s. 12232 - 14
Hlavní autoři:	Boltz, Tobias, Martinez, Jose L., Xu, Connie, Baker, Kathryn R. L., Zhu, Zihan, Morgan, Jenny, Roussel, Ryan, Ratner, Daniel, Mustapha, Brahim, Edelen, Auralee L.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	London Nature Publishing Group UK 10.04.2025 Nature Publishing Group Nature Portfolio
Témata:	639/624/1020/1087 639/766 639/766/259 639/766/419/1131 Algorithms Bayesian analysis Convergence Experimental particle physics Free-electron lasers Humanities and Social Sciences Information theory and computation Mathematical models MATHEMATICS AND COMPUTING multidisciplinary Neural networks Optimization algorithms Optimization techniques PARTICLE ACCELERATORS Particle Accelerators, Bayesian Optimization Physics Science Science (multidisciplinary)
ISSN:	2045-2322, 2045-2322
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Popis
Shrnutí:	Tuning particle accelerators is a challenging and time-consuming task that can be automated and carried out efficiently using suitable optimization algorithms, such as model-based Bayesian optimization techniques. One of the major advantages of Bayesian algorithms is the ability to incorporate prior information about beam physics and historical behavior into the model used to make control decisions. In this work, we examine incorporating prior accelerator physics information into Bayesian optimization algorithms by utilizing fast executing, neural network models trained on simulated or historical datasets as prior mean functions in Gaussian process models. We show that in ideal cases, this technique substantially increases convergence speed to optimal solutions in high-dimensional tuning parameter spaces. Additionally, we demonstrate that even in non-ideal cases, where prior models of beam dynamics do not exactly match experimental conditions, the use of this technique can still enhance convergence speed. Finally, we demonstrate how these methods can be used to improve optimization in practical applications, such as transferring information gained from beam dynamics simulations to online control of the LCLS injector, and transferring knowledge gained from experimental measurements across different operating modes, such as accelerating different ion species at the ATLAS heavy ion accelerator.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 USDOE Office of Science (SC), Basic Energy Sciences (BES). Scientific User Facilities (SUF) AC02-76SF00515; AC02-06CH11357 USDOE Office of Science (SC), Nuclear Physics (NP)
ISSN:	2045-2322 2045-2322
DOI:	10.1038/s41598-025-95297-z