  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.
