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Machine Learning Enhances Algorithms for Quantifying Non-Equilibrium Dynamics in Correlation Spectroscopy Experiments to Reach Frame-Rate-Limited Time Resolution. (arXiv:2201.07889v1 [eess.SP])
cs.LG updates on arXiv.org arxiv.org
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 …
algorithms arxiv correlation equilibrium learning machine machine learning rate time