| Title |
Machine Learning Analysis of Electron Cooler Operation for RHIC |
| Authors |
- X. Gu, A.V. Fedotov, D. Kayran
BNL, Upton, New York, USA
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| Abstract |
A regression machine learning algorithm was applied to analyze the operation data of RHIC with electron cooler LEReC during the 2020 physics run. After constructing a black-box surrogate model from the XGBoost algorithm and plotting their partial dependency plots for different operation parameters, we can find the effects of an individual parameter on the RHIC luminosity and optimize it accordingly offline.
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| Funding |
Work supported by Brookhaven Science Associates, LLC under Contract No. DE-SC0012704 with the U.S. Department of Energy. |
| Paper |
download MOPAB003.PDF [0.415 MB / 4 pages] |
| Export |
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| Conference |
IPAC2021 |
| Series |
International Particle Accelerator Conference (12th) |
| Location |
Campinas, SP, Brazil |
| Date |
24-28 May 2021 |
| Publisher |
JACoW Publishing, Geneva, Switzerland |
| Editorial Board |
Liu Lin (LNLS, Campinas, Brazil); John M. Byrd (ANL, Lemont, IL, USA); Regis Neuenschwander (LNLS, Campinas, Brazil); Renan Picoreti (LNLS, Campinas, Brazil); Volker R. W. Schaa (GSI, Darmstadt, Germany) |
| Online ISBN |
978-3-95450-214-1 |
| Online ISSN |
2673-5490 |
| Received |
14 May 2021 |
| Accepted |
25 May 2021 |
| Issue Date |
11 August 2021 |
| DOI |
doi:10.18429/JACoW-IPAC2021-MOPAB003 |
| Pages |
45-48 |
| Copyright |
 Published by JACoW Publishing under the terms of the Creative Commons Attribution 3.0 International license. Any further distribution of this work must maintain attribution to the author(s), the published article's title, publisher, and DOI. |
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