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@inproceedings{gu:ipac2021-mopab003,
author = {X. Gu and A.V. Fedotov and D. Kayran},
title = {{Machine Learning Analysis of Electron Cooler Operation for RHIC}},
booktitle = {Proc. IPAC'21},
pages = {45--48},
eid = {MOPAB003},
language = {english},
keywords = {luminosity, electron, operation, scattering, GUI},
venue = {Campinas, SP, Brazil},
series = {International Particle Accelerator Conference},
number = {12},
publisher = {JACoW Publishing, Geneva, Switzerland},
month = {08},
year = {2021},
issn = {2673-5490},
isbn = {978-3-95450-214-1},
doi = {10.18429/JACoW-IPAC2021-MOPAB003},
url = {https://jacow.org/ipac2021/papers/mopab003.pdf},
note = {https://doi.org/10.18429/JACoW-IPAC2021-MOPAB003},
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.}},
}