# Restoring Original Signal From Pile-up Signal using Deep Learning

arXiv: https://arxiv.org/abs/2304.14496 · HTML: https://mlsanity.com/paper/2304.14496/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 31 references that are in the map.
- First posted on arXiv 24 Apr 2023.

## Builds on (most cited first, 7 of 7)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://mlsanity.com/paper/1511.06434/) (2015)
- [WaveNet: A Generative Model for Raw Audio](https://mlsanity.com/paper/1609.03499/) (2016)
- [Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization](https://mlsanity.com/paper/1603.06560/) (2016)
- [Deep learning based pulse shape discrimination for germanium detectors](https://mlsanity.com/paper/1903.01462/) (2019)

## Abstract (arXiv)

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.