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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2409.03931 |
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| _version_ | 1866909307370471424 |
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| author | Henderson, M. Edelen, J. P. Einstein-Curtis, J. Hall, C. C. Cruz, J. A. Diaz Edelen, A. L. |
| author_facet | Henderson, M. Edelen, J. P. Einstein-Curtis, J. Hall, C. C. Cruz, J. A. Diaz Edelen, A. L. |
| contents | Industrial particle accelerators typically operate in dirtier environments than research accelerators, leading to increased noise in RF and electronic systems. Furthermore, given that industrial accelerators are mass produced, less attention is given to optimizing the performance of individual systems. As a result, industrial accelerators tend to underperform their own hardware capabilities. Improving signal processing for these machines will improve cost and time margins for deployment, helping to meet the growing demand for accelerators for medical sterilization, food irradiation, cancer treatment, and imaging. Our work focuses on using machine learning techniques to reduce noise in RF signals used for pulse-to-pulse feedback in industrial accelerators. Here we review our algorithms and observed results for simulated RF systems, and discuss next steps with the ultimate goal of deployment on industrial systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_03931 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Machine Learning for Reducing Noise in RF Control Signals at Industrial Accelerators Henderson, M. Edelen, J. P. Einstein-Curtis, J. Hall, C. C. Cruz, J. A. Diaz Edelen, A. L. Accelerator Physics Industrial particle accelerators typically operate in dirtier environments than research accelerators, leading to increased noise in RF and electronic systems. Furthermore, given that industrial accelerators are mass produced, less attention is given to optimizing the performance of individual systems. As a result, industrial accelerators tend to underperform their own hardware capabilities. Improving signal processing for these machines will improve cost and time margins for deployment, helping to meet the growing demand for accelerators for medical sterilization, food irradiation, cancer treatment, and imaging. Our work focuses on using machine learning techniques to reduce noise in RF signals used for pulse-to-pulse feedback in industrial accelerators. Here we review our algorithms and observed results for simulated RF systems, and discuss next steps with the ultimate goal of deployment on industrial systems. |
| title | Machine Learning for Reducing Noise in RF Control Signals at Industrial Accelerators |
| topic | Accelerator Physics |
| url | https://arxiv.org/abs/2409.03931 |