Automated RF Phase Adjustment for Beam Stabilization in the Fermilab Linac
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866914169232556032 |
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| author | Chichili, R. R. Sulskis, J. A. Sharankova, R. Vamanan, B. Ravi, S. |
| author_facet | Chichili, R. R. Sulskis, J. A. Sharankova, R. Vamanan, B. Ravi, S. |
| contents | The Fermilab Linac experiences longitudinal beam phase drift, leading to increased particle loss, conventionally corrected through labor-intensive manual RF adjustments. This project explores machine learning-based automation for drift correction, employing a prototype-based classification approach. Our model utilizes a 34-dimensional feature set (RF settings and BPM readings) and leverages a 7x27 response matrix for system modeling. To overcome limited real-world data, we generate synthetic data, enhancing model training and generalizability. Custom loss functions, including a surrogate energy-consistent loss and a temporal smoothness constraint, ensure physically plausible drift predictions. The goal is a robust system for autonomous phase adjustments, ensuring stable beam acceleration and reduced manual intervention. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_19141 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Automated RF Phase Adjustment for Beam Stabilization in the Fermilab Linac Chichili, R. R. Sulskis, J. A. Sharankova, R. Vamanan, B. Ravi, S. Accelerator Physics The Fermilab Linac experiences longitudinal beam phase drift, leading to increased particle loss, conventionally corrected through labor-intensive manual RF adjustments. This project explores machine learning-based automation for drift correction, employing a prototype-based classification approach. Our model utilizes a 34-dimensional feature set (RF settings and BPM readings) and leverages a 7x27 response matrix for system modeling. To overcome limited real-world data, we generate synthetic data, enhancing model training and generalizability. Custom loss functions, including a surrogate energy-consistent loss and a temporal smoothness constraint, ensure physically plausible drift predictions. The goal is a robust system for autonomous phase adjustments, ensuring stable beam acceleration and reduced manual intervention. |
| title | Automated RF Phase Adjustment for Beam Stabilization in the Fermilab Linac |
| topic | Accelerator Physics |
| url | https://arxiv.org/abs/2511.19141 |