2021
DOI: 10.1038/s41598-021-93747-y
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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. 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 con… Show more

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Cited by 16 publications
(9 citation statements)
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“…The described tools enable the next-generation data access, which will be used by the emerging projects using AI/ML methods [7,8,9] for fast and scoped data retrieval needed for such tools to make decisions on better adaptive data acquisition and more intelligent data analysis.…”
Section: Discussionmentioning
confidence: 99%
“…The described tools enable the next-generation data access, which will be used by the emerging projects using AI/ML methods [7,8,9] for fast and scoped data retrieval needed for such tools to make decisions on better adaptive data acquisition and more intelligent data analysis.…”
Section: Discussionmentioning
confidence: 99%
“…For this approach scattering data are typically processed to improve the conventional fitting procedures. For example, the denoising of neutron reflectivity data or XPCS data by an autoencoder has already been demonstrated (Konstantinova et al, 2021;Timmermann et al, 2022).…”
Section: Strategies and Challengesmentioning
confidence: 99%
“…Other standard scattering methods, such as small-angle scattering, have also received considerable attention, especially for classification tasks (Song et al, 2020;Ikemoto et al, 2020;Franke et al, 2018;Archibald et al, 2020;Chang et al, 2020). For nonstandard coherent scattering methods, such as X-ray photon correlation spectroscopy (XPCS), ML-based analysis using autoencoders has been employed (Konstantinova et al, 2021;Timmermann et al, 2022). For a more general review of ML methods for scattering we refer to a recent review, which also discusses applications in the broader context of scattering experiments, such as spectroscopy methods, theoretical calculations, automated alignment procedures, beam optimization and data filtering (Chen et al, 2021).…”
Section: Introductionmentioning
confidence: 99%
“…The output of the model can be utilized by subsequent AI-guided analysis and results extraction. 72 The model does not have to be updated after each new measurement is added to the folder and routine model updates can be scheduled when sufficient amount of new data are acquired and labeled. The process of model update and application can easily be automated using Papermill.…”
Section: Deployment Interfacesmentioning
confidence: 99%