Noise Reduction in X-ray Photon Correlation Spectroscopy with Convolutional Neural Networks Encoder-Decoder Models

• URL: http://arxiv.org/abs/2102.03877v1
• Date: Sun, 7 Feb 2021 18:38:59 GMT
• Title: Noise Reduction in X-ray Photon Correlation Spectroscopy with Convolutional Neural Networks Encoder-Decoder Models
• Authors: Tatiana Konstantinova, Lutz Wiegart, Maksim Rakitin, Anthony M. DeGennaro, Andi M. Barbour
• Abstract summary: We propose a computational approach for improving the signal-to-noise ratio in two-time correlation functions. CNN-ED models are based on Convolutional Neural Network-Decoder (CNN-ED) models. 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Like other experimental techniques, X-ray Photon Correlation Spectroscopy is a 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 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.

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