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@inproceedings{fol:ipac2021-thpab068,
author = {E. Fol and R. Tomás García},
title = {{Denoising of Optics Measurements Using Autoencoder Neural Networks}},
booktitle = {Proc. IPAC'21},
pages = {3915--3918},
eid = {THPAB068},
language = {english},
keywords = {optics, network, simulation, MMI, controls},
venue = {Campinas, SP, Brazil},
series = {International Particle Accelerator Conference},
number = {12},
publisher = {JACoW Publishing, Geneva, Switzerland},
month = {08},
year = {2021},
issn = {2673-5490},
isbn = {978-3-95450-214-1},
doi = {10.18429/JACoW-IPAC2021-THPAB068},
url = {https://jacow.org/ipac2021/papers/thpab068.pdf},
note = {https://doi.org/10.18429/JACoW-IPAC2021-THPAB068},
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.}},
}