Noise reduction in X-ray photon correlation spectroscopy with convolutional neural networks encoder–decoder models

Abstract Like other experimental techniques, X-ray photon correlation spectroscopy is subject to various kinds of noise. Random and correlated fluctuations and heterogeneities can be present in a two-time correlation function and obscure the information about the intrinsic dynamics of a sample. Simu...

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Autores principales:	Tatiana Konstantinova, Lutz Wiegart, Maksim Rakitin, Anthony M. DeGennaro, Andi M. Barbour
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/84de9308085047b997ea448e9d21a2de
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spelling	oai:doaj.org-article:84de9308085047b997ea448e9d21a2de2021-12-02T16:26:23ZNoise reduction in X-ray photon correlation spectroscopy with convolutional neural networks encoder–decoder models10.1038/s41598-021-93747-y2045-2322https://doaj.org/article/84de9308085047b997ea448e9d21a2de2021-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-93747-yhttps://doaj.org/toc/2045-2322Abstract Like other experimental techniques, X-ray photon correlation spectroscopy is subject to various kinds of noise. Random and correlated fluctuations and heterogeneities can be present in a two-time correlation function and obscure the information about the intrinsic dynamics of a sample. Simultaneously addressing the disparate origins of noise in the experimental data is challenging. We propose a computational approach for improving the signal-to-noise ratio in two-time correlation functions that is based on convolutional neural network encoder–decoder (CNN-ED) models. Such models extract features from an image via convolutional layers, project them to a low dimensional space and then reconstruct a clean image from this reduced representation via transposed convolutional layers. Not only are ED models a general tool for random noise removal, but their application to low signal-to-noise data can enhance the data’s quantitative usage since they are able to learn the functional form of the signal. We demonstrate that the CNN-ED models trained on real-world experimental data help to effectively extract equilibrium dynamics’ parameters from two-time correlation functions, containing statistical noise and dynamic heterogeneities. Strategies for optimizing the models’ performance and their applicability limits are discussed.Tatiana KonstantinovaLutz WiegartMaksim RakitinAnthony M. DeGennaroAndi M. BarbourNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Tatiana Konstantinova Lutz Wiegart Maksim Rakitin Anthony M. DeGennaro Andi M. Barbour Noise reduction in X-ray photon correlation spectroscopy with convolutional neural networks encoder–decoder models
description	Abstract Like other experimental techniques, X-ray photon correlation spectroscopy is subject to various kinds of noise. Random and correlated fluctuations and heterogeneities can be present in a two-time correlation function and obscure the information about the intrinsic dynamics of a sample. Simultaneously addressing the disparate origins of noise in the experimental data is challenging. We propose a computational approach for improving the signal-to-noise ratio in two-time correlation functions that is based on convolutional neural network encoder–decoder (CNN-ED) models. Such models extract features from an image via convolutional layers, project them to a low dimensional space and then reconstruct a clean image from this reduced representation via transposed convolutional layers. Not only are ED models a general tool for random noise removal, but their application to low signal-to-noise data can enhance the data’s quantitative usage since they are able to learn the functional form of the signal. We demonstrate that the CNN-ED models trained on real-world experimental data help to effectively extract equilibrium dynamics’ parameters from two-time correlation functions, containing statistical noise and dynamic heterogeneities. Strategies for optimizing the models’ performance and their applicability limits are discussed.
format	article
author	Tatiana Konstantinova Lutz Wiegart Maksim Rakitin Anthony M. DeGennaro Andi M. Barbour
author_facet	Tatiana Konstantinova Lutz Wiegart Maksim Rakitin Anthony M. DeGennaro Andi M. Barbour
author_sort	Tatiana Konstantinova
title	Noise reduction in X-ray photon correlation spectroscopy with convolutional neural networks encoder–decoder models
title_short	Noise reduction in X-ray photon correlation spectroscopy with convolutional neural networks encoder–decoder models
title_full	Noise reduction in X-ray photon correlation spectroscopy with convolutional neural networks encoder–decoder models
title_fullStr	Noise reduction in X-ray photon correlation spectroscopy with convolutional neural networks encoder–decoder models
title_full_unstemmed	Noise reduction in X-ray photon correlation spectroscopy with convolutional neural networks encoder–decoder models
title_sort	noise reduction in x-ray photon correlation spectroscopy with convolutional neural networks encoder–decoder models
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/84de9308085047b997ea448e9d21a2de
work_keys_str_mv	AT tatianakonstantinova noisereductioninxrayphotoncorrelationspectroscopywithconvolutionalneuralnetworksencoderdecodermodels AT lutzwiegart noisereductioninxrayphotoncorrelationspectroscopywithconvolutionalneuralnetworksencoderdecodermodels AT maksimrakitin noisereductioninxrayphotoncorrelationspectroscopywithconvolutionalneuralnetworksencoderdecodermodels AT anthonymdegennaro noisereductioninxrayphotoncorrelationspectroscopywithconvolutionalneuralnetworksencoderdecodermodels AT andimbarbour noisereductioninxrayphotoncorrelationspectroscopywithconvolutionalneuralnetworksencoderdecodermodels
_version_	1718384063958482944

Noise reduction in X-ray photon correlation spectroscopy with convolutional neural networks encoder–decoder models

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