Reinforcement Learning for Real-Time Luminosity Optimization in Colliders | Sciact - CRIS-system of Boreskov Institute of Catalysis

Reinforcement Learning for Real-Time Luminosity Optimization in Colliders Full article

Journal

Physical Review Accelerators and Beams
ISSN: 2469-9888

Output data

Year: 2025, Volume: 28, Article number : 122802, Pages count : 16 DOI: 10.1103/71jq-vbl6

Authors

Mamutov R. ^1,2 , Baranov G. ^1,2 , Gerasev A. ²

Affiliations

1	Budker Institute of Nuclear Physics SB RAS, Novosibirsk, 630090, Russia
2	Synchrotron Radiation Facility—Siberian Circular Photon Source “SKIF” Boreskov Institute of Catalysis SB RAS, Koltsovo, 630559, Russia

Funding (1)

Ministry of Science and Higher Education of the Russian Federation

FWUR-2025-0004

This work introduces a reinforcement learning algorithm designed for real-time luminosity optimization in collider experiments. The neural network architecture is selected from multiple candidates through systematic evaluation of their training performance. Prior to training, the input data undergo multistage preprocessing before being fed into the neural network. Our approach combines off-line pretraining on historical accelerator data with online fine-tuning during operation. By processing accelerator measurements over multisecond timescales, the reinforcement learning model dynamically adjusts the magnetic structure to maintain luminosity stability under changing beam conditions. The autonomous nature of the method eliminates the need for manual intervention, enhancing both operational efficiency and beam stability in long-term operation. Experimental validation on the VEPP-4M collider demonstrates the feasibility of the approach and provides a foundation for future development and deployment in accelerator systems.

Mamutov R. , Baranov G. , Gerasev A.
Reinforcement Learning for Real-Time Luminosity Optimization in Colliders
Physical Review Accelerators and Beams. 2025. V.28. 122802 :1-16. DOI: 10.1103/71jq-vbl6 WOS Scopus РИНЦ OpenAlex publication_identifier_short.sciact_skif_identifier_type

Submitted:	Aug 29, 2025
Accepted:	Dec 8, 2025
Published online:	Dec 29, 2025

Web of science:	WOS:001654855700001
Scopus:	2-s2.0-105026259743
Elibrary:	88723781
OpenAlex:	W4417210033
publication_identifier.sciact_skif_identifier_type:	4241

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