Pile Up Signal

Pile-up signal, the overlapping of multiple signals in detectors, presents a significant challenge in various scientific fields, particularly particle physics. Current research focuses on developing advanced signal processing techniques, including deep learning models like graph neural networks and recurrent neural networks (LSTMs), to disentangle and reconstruct the original signals from pile-up events. These methods aim to improve data quality by enhancing energy and timing resolutions, ultimately leading to more accurate measurements and improved particle identification. The successful application of these techniques has the potential to significantly improve the precision and efficiency of experiments across diverse scientific domains.