SuperKEKB positron beam tuning using machine learning
In the KEK injector linac, four-ring simultaneous top-up injection has been achieved, and beam tuning is always performed in various beam modes. As there are four beam modes, the optimum magnet current and RF phase must be selected for each. There are numerous tuning knobs for each mode; thus, it ta...
Saved in:
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2024-01-01
|
| Series: | EPJ Web of Conferences |
| Online Access: | https://www.epj-conferences.org/articles/epjconf/pdf/2024/25/epjconf_lcws2024_02004.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841557351812825088 |
|---|---|
| author | Natsui Takuya |
| author_facet | Natsui Takuya |
| author_sort | Natsui Takuya |
| collection | DOAJ |
| description | In the KEK injector linac, four-ring simultaneous top-up injection has been achieved, and beam tuning is always performed in various beam modes. As there are four beam modes, the optimum magnet current and RF phase must be selected for each. There are numerous tuning knobs for each mode; thus, it takes significant time and manpower to find the optimum state for all modes. In particular, tuning the positron primary electron beam requires delicate parameter adjustment due to its large charge. Significant time has been spent on this tuning. Therefore, an automatic tuning tool has been developed. Automatic tuning is realized using Bayesian optimization and the downhill simplex method. This tool can be used for any beam tuning on our system and has been particularly useful for positron beam tuning. |
| format | Article |
| id | doaj-art-497b9d442170409f873062dc21ed1540 |
| institution | Kabale University |
| issn | 2100-014X |
| language | English |
| publishDate | 2024-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | EPJ Web of Conferences |
| spelling | doaj-art-497b9d442170409f873062dc21ed15402025-01-06T11:33:47ZengEDP SciencesEPJ Web of Conferences2100-014X2024-01-013150200410.1051/epjconf/202431502004epjconf_lcws2024_02004SuperKEKB positron beam tuning using machine learningNatsui Takuya0KEKIn the KEK injector linac, four-ring simultaneous top-up injection has been achieved, and beam tuning is always performed in various beam modes. As there are four beam modes, the optimum magnet current and RF phase must be selected for each. There are numerous tuning knobs for each mode; thus, it takes significant time and manpower to find the optimum state for all modes. In particular, tuning the positron primary electron beam requires delicate parameter adjustment due to its large charge. Significant time has been spent on this tuning. Therefore, an automatic tuning tool has been developed. Automatic tuning is realized using Bayesian optimization and the downhill simplex method. This tool can be used for any beam tuning on our system and has been particularly useful for positron beam tuning.https://www.epj-conferences.org/articles/epjconf/pdf/2024/25/epjconf_lcws2024_02004.pdf |
| spellingShingle | Natsui Takuya SuperKEKB positron beam tuning using machine learning EPJ Web of Conferences |
| title | SuperKEKB positron beam tuning using machine learning |
| title_full | SuperKEKB positron beam tuning using machine learning |
| title_fullStr | SuperKEKB positron beam tuning using machine learning |
| title_full_unstemmed | SuperKEKB positron beam tuning using machine learning |
| title_short | SuperKEKB positron beam tuning using machine learning |
| title_sort | superkekb positron beam tuning using machine learning |
| url | https://www.epj-conferences.org/articles/epjconf/pdf/2024/25/epjconf_lcws2024_02004.pdf |
| work_keys_str_mv | AT natsuitakuya superkekbpositronbeamtuningusingmachinelearning |