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...

Full description

Saved in:
Bibliographic Details
Published in:arXiv.org
Main Authors: Konstantinova, Tatiana, Wiegart, Lutz, Rakitin, Maksim, DeGennaro, Anthony M, Barbour, Andi M
Format: Paper
Language:English
Published: Ithaca Cornell University Library, arXiv.org 05.08.2021
Subjects:
Artificial neural networks
Coders
Correlation
Encoders-Decoders
Feature extraction
Image reconstruction
Neural networks
Noise
Noise reduction
Photon correlation spectroscopy
Photons
Random noise
Signal to noise ratio
Spectrum analysis
Time correlation functions
ISSN:2331-8422
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
Bibliography:SourceType-Working Papers-1
ObjectType-Working Paper/Pre-Print-1
content type line 50
ISSN:2331-8422
DOI:10.48550/arxiv.2102.03877