Speaker
Jonathan Edelen
(RadiaSoft (United States))
Description
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.
Funding Agency
This work is supported by the Department of Energy Office of Science, Office of Accelerator Research, Development, and Production award number DE-SC0023641
| Region represented | America |
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Author
Jonathan Edelen
(RadiaSoft (United States))
Co-authors
Auralee Edelen
(SLAC National Accelerator Laboratory)
Jorge Diaz Cruz
(University of New Mexico)
Joshua Einstein-Curtis
(RadiaSoft (United States))
Kathryn Wolfinger
(RadiaSoft (United States))
Morgan Henderson
(RadiaSoft (United States))
