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| URL | https://doi.org/10.18429/JACoW-IPAC2025-MOPM092 |
|---|---|
| Title | Machine learning approach to MDI optimization for 3 TeV c.o.m. Muon Collider |
| Authors |
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| Abstract | The Muon Collider is a proposed future accelerator for very high energy muon collision. Since muons are heavier than electrons, the synchrotron radiation is negligible at this high energy, allowing to build a compact machine able to deliver Multi-TeV c.o.m. energy collisions, enabling precision measurements of the Standard Model quantities and search for new physics. A challenge of a muon beam is the Beam-Induced Background (BIB), a flux of particles in the detector generated by secondary interaction of muon decay products with the accelerator components. To deliver the required physics performance, the Machine Detector Interface design needs to include a shielding for the BIB. The proposed solution consists of cone-shaped tungsten shields inside the detector area. The nozzles reduce the BIB to a manageable level at the cost of reducing the detector acceptance. A careful optimization of the geometry is necessary to further mitigate the BIB and improving the detector acceptance to maximize the physics potential. This contribution aims at discussing the optimization achieved with machine learning algorithms in combination with FLUKA simulations for a 3 TeV c.o.m. Muon Collider. |
| Paper | download: MOPM092.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 | 04 June 2025 |
| Issued | 05 November 2025 |
| DOI | 10.18429/JACoW-IPAC2025-MOPM092 |
| Pages | 539-541 |
| 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. |