  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.
