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| URL | https://doi.org/10.18429/JACoW-IPAC2025-THPM010 |
|---|---|
| Title | Using machine learning techniques for BGI-based profile measurements at the CERN PS |
| Authors |
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| Abstract | The Beam Gas Ionization (BGI) instrument provides a non-destructive method for monitoring transverse beam profiles by detecting free electrons produced during beam-gas ionization. Utilizing a Timepix-family detector, the BGI setup at the CERN Proton Synchrotron (PS) includes two instruments dedicated to horizontal and vertical plane measurements. However, the quality of these measurements is often compromised by artifacts, such as beam losses, which degrade profile quality, make the analysis significantly more difficult and ultimately affect the instrument performance. To address these challenges, this contribution explores the application of machine learning techniques for effective background removal. Both supervised and unsupervised approaches are evaluated on data acquired from the operational systems to improve the accuracy and reliability of the reconstructed profiles. Furthermore, the computational performance and time complexity of these methods are evaluated to ensure that the proposed solutions are compatible with the operational requirements of the BGI system. |
| Paper | download: THPM010.pdf |
| Cite | BibTeX, LaTeX, Text/Word, RIS, EndNote |
| Conference | 16th International Particle Accelerator Conference |
| Series | |
| Location | Taipei, Taiwan |
| Date | 01-06 Jun 2025 |
| Publisher | JACoW Publishing, Geneva, Switzerland |
| Editorial Board | * Ming-Chyuan Lin - National Synchrotron Radiation Research Center * Yoichi Sato - KEK * Jui-Che Huang - National Synchrotron Radiation Research Center * David Button - Australian Nuclear Science and Technology Organisation * Ping-Shun Chuang - National Synchrotron Radiation Research Center |
| Online ISBN | 978-3-95450-248-6 |
| Online ISSN | 2673-5490 |
| Received | 28 May 2025 |
| Revised | 04 June 2025 |
| Accepted | 05 June 2025 |
| Issued | 05 November 2025 |
| DOI | 10.18429/JACoW-IPAC2025-THPM010 |
| Pages | 2707-2710 |
| Copyright | Published by JACoW Publishing under the terms of the Creative Commons Attribution 4.0 license. Any further distribution of this work must maintain attribution to the author(s), the published article's title, publisher, and DOI. |