Accelerator tuning method using autoencoder and Bayesian optimization

During beam-accelerator operation, a large number of parameters need to be tuned. In recent years, tuning methods based on machine learning have been extensively studied. Bayesian optimization (BO) has attracted considerable attention as an excellent method for accelerator tuning. However, its appli...

Full description

Saved in:
Bibliographic Details
Published in:Nuclear instruments & methods in physics research. Section A, Accelerators, spectrometers, detectors and associated equipment Vol. 1057; p. 168730
Main Authors: Morita, Yasuyuki, Washio, Takashi, Nakashima, Yuta
Format: Journal Article
Language:English
Published: Elsevier B.V 01.12.2023
Subjects:
Accelerator
Autoencoder
Bayesian optimization
Machine learning
Bayesian optimization
Autoencoder
Machine learning
Accelerator
ISSN:0168-9002, 1872-9576
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:During beam-accelerator operation, a large number of parameters need to be tuned. In recent years, tuning methods based on machine learning have been extensively studied. Bayesian optimization (BO) has attracted considerable attention as an excellent method for accelerator tuning. However, its applicability is limited by the number of parameters that can be tuned. In this study, we propose an optimization method that combines autoencoder and BO to tune a large number of parameters. We verified it using beam transport simulations. We confirmed a higher tuning effect in a shorter time than when using only BO. The proposed method is expected to speed up the accelerator operation and provide comprehensive tuning.
ISSN:0168-9002
1872-9576
DOI:10.1016/j.nima.2023.168730