CERN, Meyrin, Switzerland
University of Essex, Physics Centre, Colchester, United Kingdom
GSI, Darmstadt, Germany
This project has received funding from the European Union’s Horizon 2020 Research and Innovation programme under GA No 101004730.
| Paper | Title | Other Keywords | Page |
|---|---|---|---|
| WEP06 | An LHC Protection System Based on Fast Beam Intensity Drops | operation, FPGA, detector, emittance | 387 |
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| The Large Hadron Collider (LHC) is protected against potentially dangerous beam losses by a distributed system based on some four thousand beam loss monitors. To provide an additional level of safety, the LHC has been equipped with a system to detect fast beam intensity drops and trigger a beam dump for potentially dangerous rates. This paper describes the architecture of the system and its signal processing, optimized to cope with dump thresholds in the order of 0.01 % of the circulating beam intensity. The performance of the installed system is presented based upon beam measurements. | |||
| DOI • | reference for this paper ※ doi:10.18429/JACoW-IBIC2022-WEP06 | ||
| About • | Received ※ 10 September 2022 — Revised ※ 11 September 2022 — Accepted ※ 12 September 2022 — Issue date ※ 22 November 2022 | ||
| Cite • | reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml) | ||
| WEP42 | Application of Machine Learning towards Particle Counting and Identification | network, extraction, detector, experiment | 508 |
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An exploration into the application of three machine learning (ML) approaches to identify and separate events in the detectors used for particle counting at the GSI Helmholtz Centre for Heavy Ion Research. A convolutional neural network (CNN), a shape-based template matching algorithm (STMF) and Peak Property-based Counting Algorithm (PPCA) were developed to accurately count the number of particles without domain-specific knowledge required to run the currently used algorithm. The three domain-agnostic ML algorithms are based on data from scintillation counters commonly used in beam instrumentation and represent proof-of-work for an automated particle counting system. The algorithms were trained on a labelled set of over 150 000 experimental particle data. The results of the three classification approaches were compared to find a solution that best mitigates the effects of particle pile-ups. The two best-achieving algorithms were the CNN and PPCA, achieving an accuracy of 99.8\%.
This project has received funding from the European Union’s Horizon 2020 Research and Innovation programme under GA No 101004730. |
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Poster WEP42 [1.370 MB] | ||
| DOI • | reference for this paper ※ doi:10.18429/JACoW-IBIC2022-WEP42 | ||
| About • | Received ※ 11 September 2022 — Revised ※ 25 October 2022 — Accepted ※ 01 December 2022 — Issue date ※ 08 December 2022 | ||
| Cite • | reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml) | ||