  Pile-up signals are frequently produced in experimental physics. They create
inaccurate physics data with high uncertainty and cause various problems.
Therefore, the correction to pile-up signals is crucially required. In this
study, we implemented a deep learning method to restore the original signals
from the pile-up signals. We showed that a deep learning model could accurately
reconstruct the original signal waveforms from the pile-up waveforms. By
substituting the pile-up signals with the original signals predicted by the
model, the energy and timing resolutions of the data are notably enhanced. The
model implementation significantly improved the quality of the particle
identification plot and particle tracks. This method is applicable to similar
problems, such as separating multiple signals or correcting pile-up signals
with other types of noises and backgrounds.
