Noise Reduction in X-ray Photon Correlation Spectroscopy with Convolutional Neural Networks Encoder-Decoder Models
This work provides an incremental improvement in data analysis for researchers using X-ray Photon Correlation Spectroscopy, enabling more accurate extraction of sample dynamics from noisy experimental data.
This paper addresses noise in X-ray Photon Correlation Spectroscopy (XPCS) data, specifically in two-time correlation functions, which can obscure sample dynamics. The authors propose using Convolutional Neural Network Encoder-Decoder (CNN-ED) models to improve the signal-to-noise ratio, demonstrating their effectiveness in extracting equilibrium dynamics parameters from noisy experimental data.
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.