The Joint Accelerator Conferences Website (JACoW) is an international collaboration that publishes the proceedings of accelerator conferences held around the world.
@inproceedings{sun:ipac2021-tupab060,
author = {Y.P. Sun},
title = {{Machine Learning on Beam Lifetime and Top-Up Efficiency}},
booktitle = {Proc. IPAC'21},
pages = {1499--1501},
eid = {TUPAB060},
language = {english},
keywords = {network, operation, storage-ring, emittance, photon},
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-TUPAB060},
url = {https://jacow.org/ipac2021/papers/tupab060.pdf},
note = {https://doi.org/10.18429/JACoW-IPAC2021-TUPAB060},
abstract = {{Both unsupervised and supervised machine learning techniques are employed for automatic clustering, modeling and prediction of Advanced Photon Source (APS) storage ring beam lifetime and top-up efficiency archived in operations. The naive Bayes classifier algorithm is developed and combined with k-means clustering to improve accuracy, where the unsupervised clustering of APS beam lifetime and top-up efficiency is consistent with either true label from data archive or Gaussian kernel density estimation. Artificial neural network algorithms have been developed, and employed for training and modelling the arbitrary relations of beam lifetime and top-up efficiency on many observable parameters. The predictions from artificial neural network reasonably agree with the APS operation data.}},
}