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

TL;DR

This paper introduces a CNN-Encoder-Decoder approach to enhance the signal-to-noise ratio in X-ray Photon Correlation Spectroscopy data, enabling more accurate analysis of sample dynamics despite noise and heterogeneities.

Contribution

It presents a novel CNN-ED model tailored for noise reduction in two-time correlation functions, improving data quality and analysis in X-ray spectroscopy.

Findings

CNN-ED models effectively extract equilibrium dynamics parameters

Models trained on real data improve noise suppression

Enhanced data allows better quantitative analysis

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 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.