Author:
Morita Y,Fukuda M,Yorita T,Kanda H,Hatanaka K,Saitou T,Tamura H,Yasuda Y,Washio T,Nakashima Y,Iwasaki M,Koay H W,Takeda K,Hara T,Chong T H,Zhao H
Abstract
Abstract
Various factors influence each other in an ion source. Therefore, when operating an ion source, it is necessary to optimize and adjust various parameters. This time, we performed an experiment to automize adjustment that maximizes the brightness of the beam using machine learning. By automatically adjusting 4 parameters, we succeeded in finding a point with a beam brightness of 4.32 × 10-6 mA/(imm mrad)2 in 25 steps. This shows that automatic adjustment using Bayesian optimization is feasible.
Subject
General Physics and Astronomy
Cited by
2 articles.
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1. Accelerator tuning method using autoencoder and Bayesian optimization;Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment;2023-12
2. Two Dimensional TM010 Axially Symmetric RF Cavity Solver Based on Convolutional Neural Network;2023 International Conference on Radar, Antenna, Microwave, Electronics, and Telecommunications (ICRAMET);2023-11-15