| Title |
Optics Corrections Using Machine Learning in the LHC |
| Authors |
- E. Fol, J.M. Coello de Portugal, R. Tomás
CERN, Meyrin, Switzerland
- G. Franchetti
GSI, Darmstadt, Germany
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| Abstract |
Optics corrections in the LHC are based on a response matrix approach between available correctors and observables. Supervised learning has been applied to quadrupole error prediction at the LHC giving promising results in simulations and surpassing the performance of the traditional approach. A comparison of different algorithms is given and it is followed by the presentation of further possible concepts to obtain optics corrections using machine learning.
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| Paper |
download THPRB077.PDF [0.414 MB / 4 pages] |
| Export |
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| 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 |
14 May 2019 |
| Accepted |
21 May 2019 |
| Issue Date |
21 June 2019 |
| DOI |
doi:10.18429/JACoW-IPAC2019-THPRB077 |
| Pages |
3990-3993 |
| 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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