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| URL | https://doi.org/10.18429/JACoW-IPAC2025-THPS071 |
|---|---|
| Title | Analysis of noise spectra color on machine learning denoising algorithms |
| Authors |
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| Abstract | Previous work has shown the efficacy of using machine learning for removal of noise in LLRF signals when operating in an industrial environment. Here we extend the analysis to include different noise power spectra. Specifically we analyze the impact on denoisig when correlated noise power spectra are used. Four different noise spectra are analyzed including red, pink, violet, and blue noise. We demonstrate the ability to remove the noise when trained on only white noise and compare this to results when retraining on different color spectra. |
| Paper | download: THPS071.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 | 05 June 2025 |
| Accepted | 05 June 2025 |
| Issued | 05 November 2025 |
| DOI | 10.18429/JACoW-IPAC2025-THPS071 |
| Pages | 3112-3114 |
| 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. |