1–6 Jun 2025
Taipei International Convention Center (TICC)
Asia/Taipei timezone

Real-time luminosity optimization in collider experiments using reinforcement learning

THPS072
5 Jun 2025, 15:30
2h
Exhibiton Hall A _Salmon (TWTC)

Exhibiton Hall A _Salmon

TWTC

Poster Presentation MC6.D13 Machine Learning Thursday Poster Session

Speaker

Rasim Mamutov (Russian Academy of Sciences)

Description

This study presents the development and implementation of a reinforcement learning-based algorithm for real-time luminosity tuning in collider experiments. The algorithm is initially pretrained on historical collider data and subsequently fine-tuned online during experiments. By analyzing accelerator measurements collected over several seconds, the model adjusts the magnetic structure to stabilize luminosity under varying experimental conditions. The proposed method allows for adaptive optimization without operator involvement, improving operational efficiency and stability. Results from its application on the VEPP-4M collider are presented, showcasing the method's feasibility and offering insights for its future development and application in accelerator systems.

Funding Agency

This work was partially supported by the Ministry of Science and Higher Education of the Russian Federation within the governmental order for Boreskov Institute of Catalysis (project FWUR-2024-0041).

Region represented Asia
Paper preparation format LaTeX

Author

Rasim Mamutov (Russian Academy of Sciences)

Co-authors

Alexey Gerasev (Russian Academy of Sciences) Grigory Baranov (Russian Academy of Sciences)

Presentation materials

There are no materials yet.