| Title |
Adding Data Science and More Intelligence to Our Accelerator Toolbox |
| Authors |
- S. Biedron
University of New Mexico, Albuquerque, USA
- S. Biedron
Element Aero, Chicago, USA
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| Abstract |
Requirements for recent accelerators are becoming more and more stringent and sophisticated machine tuning is necessary. A large amount of data is acquired from accelerator components as an assistant of machine tuning. It is hard for operators to utilize all the accelerator data for machine tuning. Therefore, machine learning, data mining and big data handling are recently applied to accelerators. For instance, Bayesian optimization is used for maximizing a target performance, a clustering algorithm is used for anomaly detection, and hidden correlation finding is utilized for discovering new aspects of a machine. This talk reviews recent progress of machine learning applications and big data handling in accelerators.
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| Paper |
download TUZPLM1.PDF [0.361 MB / 7 pages] |
| Slides |
download TUZPLM1_TALK.PDF [11.978 MB] |
| Export |
download ※ BibTeX
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※ EndNote |
| Conference |
IPAC2019 |
| Series |
International Particle Accelerator Conference (10th) |
| Location |
Melbourne, Australia |
| Date |
19-24 May 2019 |
| Publisher |
JACoW Publishing, Geneva, Switzerland |
| Editorial Board |
Mark Boland (UoM, Saskatoon, SK, Canada); Hitoshi Tanaka (KEK, Tsukuba, Japan); David Button (ANSTO, Kirrawee, NSW, Australia); Rohan Dowd (ANSTO, Kirrawee, NSW, Australia); Volker RW Schaa (GSI, Darmstadt, Germany); Eugene Tan (ANSTO, Kirrawee, NSW, Australia) |
| Online ISBN |
978-3-95450-208-0 |
| Received |
20 May 2019 |
| Accepted |
16 June 2019 |
| Issue Date |
21 June 2019 |
| DOI |
doi:10.18429/JACoW-IPAC2019-TUZPLM1 |
| Pages |
1191-1197 |
| 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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