Machine Learning Enhances Algorithms for Quantifying Non-Equilibrium Dynamics in Correlation Spectroscopy Experiments to Reach Frame-Rate-Limited Time Resolution
This work addresses the need for automated online analysis in high-data-rate experiments for researchers in correlation spectroscopy, though it appears incremental as it builds on existing algorithms with a machine learning enhancement.
The researchers tackled the problem of analyzing X-ray Photon Correlation Spectroscopy data for non-equilibrium dynamics, which often suffers from manual binning leading to loss of temporal resolution and systematic errors, by integrating a denoising autoencoder model to achieve frame-rate-limited time resolution and improve quantitative data usage.
Analysis of X-ray Photon Correlation Spectroscopy (XPCS) data for non-equilibrium dynamics often requires manual binning of age regions of an intensity-intensity correlation function. This leads to a loss of temporal resolution and accumulation of systematic error for the parameters quantifying the dynamics, especially in cases with considerable noise. Moreover, the experiments with high data collection rates create the need for automated online analysis, where manual binning is not possible. Here, we integrate a denoising autoencoder model into algorithms for analysis of non-equilibrium two-time intensity-intensity correlation functions. The model can be applied to an input of an arbitrary size. Noise reduction allows to extract the parameters that characterize the sample dynamics with temporal resolution limited only by frame rates. Not only does it improve the quantitative usage of the data, but it also creates the potential for automating the analytical workflow. Various approaches for uncertainty quantification and extension of the model for anomalies detection are discussed.