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

Optimizing collimator positions using bayesian optimization in the Fermilab MI-8 transfer line

THPM012
5 Jun 2025, 15:30
2h
Exhibiton Hall A _Magpie (TWTC)

Exhibiton Hall A _Magpie

TWTC

Poster Presentation MC6.D13 Machine Learning Thursday Poster Session

Speaker

Betiay Babacan (Fermi National Accelerator Laboratory)

Description

Collimators are used to minimize losses and to remove particles that would otherwise get lost downstream and irradiate the machine. Finding the optimal jaw positions is time consuming and with the upstream beam properties changing, the collimation settings would need to be readjusted each time. Therefore, a method to optimize collimator positions and to operate them at full capacity in a short time is required for loss control downstream. A study of collimator positions was conducted and a machine learning (ML) model was developed to predict optimal collimator positions. Bayesian Optimization (BO) was used to calculate new jaw positions from the ML model. The results of BO and usage of ML for better performance of the collimation system are presented in this paper.

Region represented America
Paper preparation format LaTeX

Author

Betiay Babacan (Fermi National Accelerator Laboratory)

Co-authors

Kyle Hazelwood (Fermi National Accelerator Laboratory) Robert Ainsworth (Fermi National Accelerator Laboratory) Pavel Snopok (Illinois Institute of Technology)

Presentation materials

There are no materials yet.