JACoW is a publisher in Geneva, Switzerland that publishes the proceedings of accelerator conferences held around the world by an international collaboration of editors.
@inproceedings{narayanan:napac2022-mopa75,
author = {A. Narayanan and J.M.S. Arnold and M.R. Austin and J.R. Berlioz and P.M. Hanlet and K.J. Hazelwood and M.A. Ibrahim and J. Jiang and H. Liu and S. Memik and V.P. Nagaslaev and D.J. Nicklaus and G. Pradhan and P.S. Prieto and A.L. Saewert and B.A. Schupbach and K. Seiya and R. Shi and M. Thieme and R.M. Thurman-Keup and N.V. Tran and D. Ulusel},
% author = {A. Narayanan and J.M.S. Arnold and M.R. Austin and J.R. Berlioz and P.M. Hanlet and K.J. Hazelwood and others},
% author = {A. Narayanan and others},
title = {{Machine Learning for Slow Spill Regulation in the Fermilab Delivery Ring for Mu2e}},
& booktitle = {Proc. NAPAC'22},
booktitle = {Proc. 5th Int. Particle Accel. Conf. (NAPAC'22)},
pages = {214--217},
eid = {MOPA75},
language = {english},
keywords = {controls, extraction, quadrupole, experiment, target},
venue = {Albuquerque, NM, USA},
series = {International Particle Accelerator Conference},
number = {5},
publisher = {JACoW Publishing, Geneva, Switzerland},
month = {10},
year = {2022},
issn = {2673-7000},
isbn = {978-3-95450-232-5},
doi = {10.18429/JACoW-NAPAC2022-MOPA75},
url = {https://jacow.org/napac2022/papers/mopa75.pdf},
abstract = {{A third-integer resonant slow extraction system is being developed for the Fermilab’s Delivery Ring to deliver protons to the Mu2e experiment. During a slow extraction process, the beam on target is liable to experience small intensity variations due to many factors. Owing to the experiment’s strict requirements in the quality of the spill, a Spill Regulation System (SRS) is currently under design. The SRS primarily consists of three components - slow regulation, fast regulation, and harmonic content tracker. In this presentation, we shall present the investigations of using Machine Learning (ML) in the fast regulation system, including further optimizations of PID controller gains for the fast regulation, prospects of an ML agent completely replacing the PID controller using supervised learning schemes such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) ML models, the simulated impact and limitation of machine response characteristics on the effectiveness of both PID and ML regulation of the spill. We also present here nascent results of Reinforcement Learning efforts, including continuous-action soft actor-critic methods, to regulate the spill rate.}},
}