| Title |
A Novel Method for Detecting Unidentified Falling Object Loss Patterns in the LHC |
| Authors |
- L. Coyle, F. Blanc, D. Di Croce, T. Pieloni
EPFL, Lausanne, Switzerland
- L. Coyle, A. Lechner, D. Mirarchi, M. Solfaroli Camillocci, J. Wenninger
CERN, Meyrin, Switzerland
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| Abstract |
Understanding and mitigating particle losses in the Large Hadron Collider (LHC) is essential for both machine safety and efficient operation. Abnormal loss distributions are telltale signs of abnormal beam behaviour or incorrect machine configuration. By leveraging the advancements made in the field of Machine Learning, a novel data-driven method of detecting anomalous loss distributions during machine operation has been developed. A neural network anomaly detection model was trained to detect Unidentified Falling Object events using stable beam, Beam Loss Monitor (BLM) data acquired during the operation of the LHC. Data-driven models, such as the one presented, could lead to significant improvements in the autonomous labelling of abnormal loss distributions, ultimately bolstering the ever ongoing effort toward improving the understanding and mitigation of these events.
|
| Paper |
download TUPOST043.PDF [2.888 MB / 4 pages] |
| Cite |
download ※ BibTeX
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※ EndNote |
| Conference |
IPAC2022 |
| Series |
International Particle Accelerator Conference (13th) |
| Location |
Bangkok, Thailand |
| Date |
12-17 June 2022 |
| Publisher |
JACoW Publishing, Geneva, Switzerland |
| Editorial Board |
Frank Zimmermann (CERN, Meyrin, Switzerland); Hitoshi Tanaka (RIKEN, Hyogo, Japan); Porntip Sudmuang (SRLI, Nakhon, Thailand); Prapong Klysubun (SRLI, Nakhon, Thailand); Prapaiwan Sunwong (SRLI, Nakhon, Thailand); Thakonwat Chanwattana (SRLI, Nakhon, Thailand); Christine Petit-Jean-Genaz (CERN, Meyrin, Switzerland); Volker R.W. Schaa (GSI, Darmstadt, Germany) |
| Online ISBN |
978-3-95450-227-1 |
| Online ISSN |
2673-5490 |
| Received |
19 May 2022 |
| Revised |
15 June 2022 |
| Accepted |
16 June 2022 |
| Issue Date |
21 June 2022 |
| DOI |
doi:10.18429/JACoW-IPAC2022-TUPOST043 |
| Pages |
953-956 |
| 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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