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| URL | https://doi.org/10.18429/JACoW-IPAC2025-THPM024 |
|---|---|
| Title | Machine learning-driven longitudinal phase space reconstruction for enhanced beam tuning at LANSCE |
| Authors |
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| Abstract | The Los Alamos Neutron Science Center (LANSCE) relies on accurate tuning of its Drift Tube Linacs (DTLs) to maintain beam quality and operational efficiency. This work introduces a novel machine-learning-based approach to reconstruct the longitudinal phase space (LPS) at the entrance of DTL Tank 1 using two-dimensional phase scans from Tanks 1 and 2. A Deep Neural Network trained on synthetic datasets generated by GPU-accelerated simulations integrates real-time diagnostic data to infer high-resolution LPS distributions. By solving this inverse problem efficiently, the method improves beam delivery precision while reducing operator intervention. Early results indicate that this approach can enhance LANSCE’s operational capabilities, providing a robust framework for accelerator tuning and diagnostics. |
| Paper | download: THPM024.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 | 03 June 2025 |
| Accepted | 04 June 2025 |
| Issued | 05 November 2025 |
| DOI | 10.18429/JACoW-IPAC2025-THPM024 |
| Pages | 2733-2736 |
| 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. |