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

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. Simultaneousl...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:Scientific reports Ročník 11; číslo 1; s. 14756 - 12
Hlavní autori: Konstantinova, Tatiana, Wiegart, Lutz, Rakitin, Maksim, DeGennaro, Anthony M., Barbour, Andi M.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: London Nature Publishing Group UK 20.07.2021
Nature Publishing Group
Nature Portfolio
Predmet:
639/301/930/12
639/705/1046
Computer applications
Experimental data
Humanities and Social Sciences
MATERIALS SCIENCE
multidisciplinary
Neural networks
Noise
Noise reduction
Science
Science (multidisciplinary)
Spectroscopy
Spectrum analysis
Statistical analysis
ISSN:2045-2322, 2045-2322
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí: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.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
USDOE Office of Science (SC), Basic Energy Sciences (BES)
BNL-222074-2021-JAAM
20-038; SC0012704
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-021-93747-y