Deep learning-based statistical noise reduction for multidimensional spectral data

Abstract

In spectroscopic experiments, data acquisition in multi-dimensional phase space may require long acquisition time, owing to the large phase space volume to be covered. In such case, the limited time available for data acquisition can be a serious constraint for experiments in which multidimensional spectral data are acquired. Here, taking angle-resolved photoemission spectroscopy (ARPES) as an example, we demonstrate a denoising method that utilizes deep learning as an intelligent way to overcome the constraint. With readily available ARPES data and random generation of training data set, we successfully trained the denoising neural network without overfitting. The denoising neural network can remove the noise in the data while preserving its intrinsic information. We show that the denoising neural network allows us to perform similar level of second-derivative and line shape analysis on data taken with two orders of magnitude less acquisition time. The importance of our method lies in its applicability to any multidimensional spectral data that are susceptible to statistical noise.

8 pages, 8 figures

References in corpus (8)

• A precise method for visualizing dispersive features in image plots
• Observation of two distinct $d_{xz}/d_{yz}$ band splittings in FeSe
• Bare electron dispersion from photoemission experiments
• Self-consistent self-energy analysis of photoemission data
• Neural Network-based Classification of Crystal Symmetries from X-Ray Diffraction Patterns
• Effects, Determination, and Correction of Count Rate Nonlinearity in Multi-Channel Analog Electron Detectors
• High repetition pump-and-probe photoemission spectroscopy based on a compact fiber laser system
• Through a Lattice Darkly -- Shedding Light on Electron-Phonon Coupling in the High T${}_c$ Cuprates