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Analysis of X-ray Photon Correlation Spectroscopy (XPCS) data fornon-equilibrium dynamics often requires manual binning of age regions of anintensity-intensity correlation function. This leads to a loss of temporalresolution and accumulation of systematic error for the parameters quantifyingthe dynamics, especially in cases with considerable noise. Moreover, theexperiments with high data collection rates create the need for automatedonline analysis, where manual binning is not possible. Here, we integrate adenoising autoencoder model into algorithms for analysis of non-equilibriumtwo-time intensity-intensity correlation functions. The model can be applied toan input of an arbitrary size. Noise reduction allows to extract the parametersthat characterize the sample dynamics with temporal resolution limited only byframe rates. Not only does it improve the quantitative usage of the data, butit also creates the potential for automating the analytical workflow. Variousapproaches for uncertainty quantification and extension of the model foranomalies detection are discussed.

Machine Learning Enhances Algorithms for Quantifying Non-Equilibrium  Dynamics in Correlation Spectroscopy Experiments to Reach Frame-Rate-Limited  Time Resolution