Removing grid structure in angle-resolved photoemission spectra via deep learning method
This addresses a specific data quality issue in spectroscopy experiments, offering a potential solution for enhancing spectral measurements across various domains, though it appears incremental as an improvement over existing filtering methods.
The paper tackles the problem of removing unwanted grid-like structures in angle-resolved photoemission spectra caused by a wire mesh, proposing a deep learning method that overcomes information loss from traditional Fourier filtering and simultaneously removes noise, optimizing spectral quality.
Spectroscopic data may often contain unwanted extrinsic signals. For example, in ARPES experiment, a wire mesh is typically placed in front of the CCD to block stray photo-electrons, but could cause a grid-like structure in the spectra during quick measurement mode. In the past, this structure was often removed using the mathematical Fourier filtering method by erasing the periodic structure. However, this method may lead to information loss and vacancies in the spectra because the grid structure is not strictly linearly superimposed. Here, we propose a deep learning method to effectively overcome this problem. Our method takes advantage of the self-correlation information within the spectra themselves and can greatly optimize the quality of the spectra while removing the grid structure and noise simultaneously. It has the potential to be extended to all spectroscopic measurements to eliminate other extrinsic signals and enhance the spectral quality based on the self-correlation of the spectra solely.