Restoring original signals from pile-up using deep learning

Abstract

Pile-up signals are frequently produced in experimental physics. They create inaccurate physics data with high uncertainties and cause multiple problems. Therefore, the correction of pile-up signals is crucially required. In this study, we implemented a deep learning method to restore the original signals from signals piled up with unwanted 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 resolution 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.