Using Machine Learning for noise reduction in X-ray Photon Correlation Spectroscopy data to quantify time series dynamics

Computational methods of noise reduction in data allow for reliable extraction of useful signals from a limited amount of experimental data. This opens the door for optimal use of experimental resources and obtaining information from intrinsically limited, e.g. destructive or out-of-equilibrium, measurements. Here, I present the application of the convolutional deep learning models for the reduction of noise in intensity-intensity correlation functions from X-ray Photon Correlation Spectroscopy (XPCS) experiments. This approach creates a filter tailored to specific types of noise encountered in XPCS experiments and results in up to 20-fold reduction of required experimental data.