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@inproceedings{gao:ipac2021-wepab306,
author = {Y. Gao and K.A. Brown and P.S. Dyer and S. Seletskiy and H. Zhao},
title = {{Applying Machine Learning to Optimization of Cooling Rate at Low Energy RHIC Electron Cooler}},
booktitle = {Proc. IPAC'21},
pages = {3391--3394},
eid = {WEPAB306},
language = {english},
keywords = {electron, simulation, network, experiment, emittance},
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-WEPAB306},
url = {https://jacow.org/ipac2021/papers/wepab306.pdf},
note = {https://doi.org/10.18429/JACoW-IPAC2021-WEPAB306},
abstract = {{The Low Energy RHIC electron Cooler (LEReC) is a novel, state-of-the-art, electron accelerator for cooling RHIC ion beams, which was recently built and commissioned. Optimization of cooling with LEReC requires fine-tuning of numerous LEReC parameters. In this work, initial optimization results of using Machine Learning (ML) methods - Bayesian Optimization (BO) and Q-learning are presented. Specially, we focus on exploring the influence of the electron trajectory on the cooling rate. In the first part, simulations are conducted by utilizing a LEReC simulator. The results show that both methods have the capability of deriving electron positions that can optimize the cooling rate. Moreover, BO takes fewer samples to converge than the Q-learning method. In the second part, Bayesian optimization is further trained on the historical cooling data. In the new samples generated by the BO, the percentage of larger cooling rates data is greatly enhanced compared with the original historical data.}},
}