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’s 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.
Highlights
Like other experimental techniques, X-ray photon correlation spectroscopy is subject to various kinds of noise
X-ray photon correlation spectroscopy (XPCS)[1,2,3] is a statistics-based technique that extracts information about a sample’s dynamics through spatial and temporal analysis of intensity correlations between sequential images of a speckled pattern collected from a coherent X-ray beam scattered from the sample
The models are trained using data from the measurements of equilibrium dynamics of nanoparticle filled polymer systems conducted at the Coherent Hard X-ray Scattering (CHX) beamline at NSLSII
Summary
X-ray photon correlation spectroscopy is subject to various kinds of noise. 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 reconstruct a clean image from this reduced representation via transposed convolutional layers. We demonstrate that the CNN-ED models trained on real-world experimental data help to effectively extract equilibrium dynamics’ parameters from twotime correlation functions, containing statistical noise and dynamic heterogeneities. Noise reduction in experiments facilitates reliable extraction of useful information from a smaller amount of data This allows for more efficient use of experimental and analytical resources as well as enables the study of systems with intrinsically limited measurement time, e.g. cases with sample damage or out-of-equilibrium dynamics. While 1TCF can be directly obtained from raw d ata[7], calculating 2TCF as Scientific Reports | (2021) 11:14756
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
