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1.
Interpretable Anomaly Detection in the LHC Main Dipole Circuits With Nonnegative Matrix Factorization / Obermair, Christoph (CERN ; Graz, Tech. U.) ; Apollonio, Andrea (CERN) ; Charifoulline, Zinour (CERN) ; Felsberger, Lukas (CERN) ; Janitschke, Marvin (CERN ; U. Rostock) ; Pernkopf, Franz (Graz, Tech. U.) ; Ravaioli, Emmanuele (CERN) ; Verweij, Arjan (CERN) ; Wollmann, Daniel (CERN) ; Wozniak, Mariusz (CERN)
CERN's Large Hadron Collider (LHC), with its eight superconducting main dipole circuits, has been in operation for over a decade. During this time, relevant operational parameters of the circuits, including circuit current, voltages across magnets and their coils, and current to ground, have been recorded. [...]
2024 - 12 p. - Published in : IEEE Trans. Appl. Supercond. 34 (2024) 4004112 Fulltext: PDF;
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2.
Interpretable Fault Prediction for CERN Energy Frontier Colliders / Obermair, Christoph
The Large Hadron Collider (LHC) is the world’s highest energy particle collider, which has already delivered data for numerous physical discoveries [...]
CERN-THESIS-2023-321 - 151.

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3.
Workshop on Dust Charging and Beam-Dust Interaction in Particle Accelerators / Blaszkiewicz, Milosz Robert (CERN) ; Felsberger, Lukas (CERN) ; Hernalsteens, Cedric (CERN) ; Heron, Jack (CERN) ; Obermair, Christoph (Graz University of Technology (AT)) ; Rosaz, Guillaume Jonathan (CERN) ; Schmidt, Rudiger (GSI - Helmholtzzentrum fur Schwerionenforschung GmbH (DE)) ; Uythoven, Jan (CERN) ; Wiesner, Christoph (CERN) ; Wollmann, Daniel (CERN) et al.
The effects of beam-dust interactions have been observed in particle accelerators and have been studied as early as the 1960s. [...]
CERN-ACC-NOTE-2023-0025. - 2023.
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4.
Data Augmentation for Breakdown Prediction in CLIC RF Cavities / Bovbjerg, Holger (CERN ; Aalborg U.) ; Apollonio, Andrea (CERN) ; Cartier-Michaud, Thomas (CERN) ; Millar, William (CERN) ; Obermair, Christoph (CERN ; Graz, Tech. U.) ; Shen, Ming (CERN ; Aalborg U.) ; Tan, Zheng-Hua (CERN ; Aalborg U.) ; Wollmann, Daniel (CERN)
One of the primary limitations on the achievable accelerating gradient in normal-conducting accelerator cavities is the occurrence of vacuum arcs, also known as RF breakdowns. A recent study on experimental data from the CLIC XBOX2 test stand at CERN proposes the use of supervised machine learning methods for predicting RF breakdowns. [...]
2022 - 4 p. - Published in : JACoW IPAC 2022 (2022) 1553-1556 Fulltext: PDF;
In : 13th International Particle Accelerator Conference (IPAC 2022), Bangkok, Thailand, 12 - 17 Jun 2022, pp.1553-1556
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5.
Anomaly Detection in Conditioning Procedures / Hofmann-Wellenhof, Martin (Graz University of Technology) ; Obermair, Christoph (Graz University of Technology (AT))
In this report, we summarize our approach to find anomalies in conditioning procedures with an LSTM autoencoder. [...]
CERN-OPEN-2022-010. - 2022. - 22 p.
Preprint
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6.
Machine Learning Models for Breakdown Prediction in RF Cavities for Accelerators / Obermair, Christoph (CERN ; Graz, Tech. U.) ; Apollonio, Andrea (CERN) ; Cartier-Michaud, Thomas (CERN) ; Catalán Lasheras, Nuria (CERN) ; Felsberger, Lukas (CERN) ; Millar, William L (CERN) ; Pernkopf, Franz (CERN ; Graz, Tech. U.) ; Wuensch, Walter (CERN)
Radio Frequency (RF) breakdowns are one of the most prevalent limits in RF cavities for particle accelerators. During a breakdown, field enhancement associated with small deformations on the cavity surface results in electrical arcs. [...]
Geneva : JACoW, 2021 - 4 p. - Published in : JACoW IPAC 2021 (2021) 1068-1071 Fulltext: PDF;
In : 12th International Particle Accelerator Conference (IPAC 2021), Online, 24 - 28 May 2021, pp.1068-1071
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7.
Machine Learning with a Hybrid Model for Monitoring of the Protection Systems of the LHC / Obermair, Christoph (CERN ; Graz, Tech. U.) ; Apollonio, Andrea (CERN) ; Charifoulline, Zinour (CERN) ; Maciejewski, Michal (CERN) ; Pernkopf, Franz (Graz, Tech. U.) ; Verweij, Arjan (CERN)
The LHC is the world’s largest particle accelerator and uses a complex set of sophisticated and highly reliable machine protection systems to ensure a safe operation with high availability for particle physics production. The data gathered during several years of successful operation allow the use of data-driven methods to assist experts in finding anomalies in the behavior of those protection systems. [...]
Geneva : JACoW, 2021 - 4 p. - Published in : JACoW IPAC 2021 (2021) 1072-1075 Fulltext: PDF;
In : 12th International Particle Accelerator Conference (IPAC 2021), Online, 24 - 28 May 2021, pp.1072-1075
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8.
Explainable Machine Learning for Breakdown Prediction in High Gradient RF Cavities / Obermair, Christoph (CERN ; Graz U.) ; Cartier-Michaud, Thomas (CERN) ; Apollonio, Andrea (CERN) ; Millar, William (CERN) ; Felsberger, Lukas (CERN) ; Fischl, Lorenz (CERN) ; Bovbjerg, Holger Severin (CERN) ; Wollmann, Daniel (CERN) ; Wuensch, Walter (CERN) ; Catalan-Lasheras, Nuria (CERN) et al.
The occurrence of vacuum arcs or radio frequency (rf) breakdowns is one of the most prevalent factors limiting the high-gradient performance of normal conducting rf cavities in particle accelerators. In this paper, we search for the existence of previously unrecognized features related to the incidence of rf breakdowns by applying a machine learning strategy to high-gradient cavity data from CERN's test stand for the Compact Linear Collider (CLIC). [...]
arXiv:2202.05610.- 2022-10-03 - 18 p. - Published in : Phys. Rev. Accel. Beams 25 (2022) 104601 Fulltext: PDF;
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9.
Extension of Signal Monitoring Applications with Machine Learning / Obermair, Christoph
The Large Hadron Colider (LHC) is the world’s largest particle accelerator [...]
CERN-THESIS-2020-010 - Graz : Obermair Christoph, 2020-02-28. - 81 p.

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10.
Signal monitoring for the LHC - Development of an application for analyzing the main quadrupole busbar resistance / Obermair, Christoph
In 2008 a faulty interconnection between two superconducting dipole magnets in the LHC led to an arc. [...]
CERN-STUDENTS-Note-2018-156. - 2018
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