Journal Article PUBDB-2024-06977

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png
Large language models for human-machine collaborative particle accelerator tuning through natural language

 ;  ;

2025
Assoc. Washington, DC [u.a.]

Science advances 11(1), eadr4173 () [10.1126/sciadv.adr4173]
 GO

This record in other databases:          

Please use a persistent id in citations: doi:  doi:

Abstract: Autonomous tuning of particle accelerators is an active and challenging research field with the goal of enabling advanced accelerator technologies and cutting-edge high-impact applications, such as physics discovery, cancer research, and material sciences. A challenge with autonomous accelerator tuning remains that the most capable algorithms require experts in optimization and machine learning to implement them for every new tuning task. Here, we propose the use of large language models (LLMs) to tune particle accelerators. We demonstrate on a proof-of-principle example the ability of LLMs to tune an accelerator subsystem based on only a natural language prompt from the operator, and compare their performance to state-of-the-art optimization algorithms, such as Bayesian optimization and reinforcement learning–trained optimization. In doing so, we also show how LLMs can perform numerical optimization of a nonlinear real-world objective. Ultimately, this work represents another complex task that LLMs can solve and promises to help accelerate the deployment of autonomous tuning algorithms to day-to-day particle accelerator operations.

Classification:

Contributing Institute(s):
  1. Strahlkontrollen (MSK)
Research Program(s):
  1. 621 - Accelerator Research and Development (POF4-621) (POF4-621)
  2. InternLabs-0011 - HIR3X - Helmholtz International Laboratory on Reliability, Repetition, Results at the most advanced X-ray Sources (2020_InternLabs-0011) (2020_InternLabs-0011)
Experiment(s):
  1. Accelerator Research Experiment at SINBAD

Appears in the scientific report 2025
Database coverage:
Medline ; Creative Commons Attribution CC BY 4.0 ; DOAJ ; OpenAccess ; Article Processing Charges ; Clarivate Analytics Master Journal List ; Current Contents - Physical, Chemical and Earth Sciences ; DOAJ Seal ; Ebsco Academic Search ; Essential Science Indicators ; Fees ; IF >= 10 ; JCR ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection ; Zoological Record
Click to display QR Code for this record

The record appears in these collections:
Private Collections > >DESY > >M > MSK
Document types > Articles > Journal Article
Public records
Publication Charges
Publications database
OpenAccess


Linked articles:

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Preprint  ;  ;
Large Language Models for Human-Machine Collaborative Particle Accelerator Tuning through Natural Language
[10.3204/PUBDB-2024-01644]  GO OpenAccess  Download fulltext Files  Download fulltextFulltext by arXiv.org BibTeX | EndNote: XML, Text | RIS


 Record created 2024-11-21, last modified 2025-07-15