| Title |
Denoising of Optics Measurements Using Autoencoder Neural Networks |
| Authors |
- E. Fol, R. Tomás García
CERN, Meyrin, Switzerland
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| Abstract |
Noise artefacts can appear in optics measurements data due to instrumentation imperfections or uncertainties in the applied analysis methods. A special type of semi-supervised neural networks, autoencoders, are widely applied to denoising tasks in image and signal processing as well as to generative modeling. Recently, an autoencoder-based approach for denoising and reconstruction of missing data has been developed to improve the quality of phase measurements obtained from harmonic analysis of LHC turn-by-turn data. We present the results achieved on simulations demonstrating the potential of the new method and discuss the effect of the noise in light of optics corrections computed from the cleaned data.
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| Paper |
download THPAB068.PDF [0.299 MB / 4 pages] |
| Poster |
download THPAB068_POSTER.PDF [0.881 MB] |
| Export |
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| Conference |
IPAC2021 |
| Series |
International Particle Accelerator Conference (12th) |
| Location |
Campinas, SP, Brazil |
| Date |
24-28 May 2021 |
| Publisher |
JACoW Publishing, Geneva, Switzerland |
| Editorial Board |
Liu Lin (LNLS, Campinas, Brazil); John M. Byrd (ANL, Lemont, IL, USA); Regis Neuenschwander (LNLS, Campinas, Brazil); Renan Picoreti (LNLS, Campinas, Brazil); Volker R. W. Schaa (GSI, Darmstadt, Germany) |
| Online ISBN |
978-3-95450-214-1 |
| Online ISSN |
2673-5490 |
| Received |
19 May 2021 |
| Accepted |
13 July 2021 |
| Issue Date |
02 September 2021 |
| DOI |
doi:10.18429/JACoW-IPAC2021-THPAB068 |
| Pages |
3915-3918 |
| Copyright |
 Published by JACoW Publishing under the terms of the Creative Commons Attribution 3.0 International license. Any further distribution of this work must maintain attribution to the author(s), the published article's title, publisher, and DOI. |
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