Dataset for neutron and gamma-ray pulse shape discrimination: radiation pulse signals and discrimination methodologies

This dataset provides neutron and gamma-ray pulse signals for pulse shape discrimination experiments. Serval traditional and recently proposed pulse shape discrimination algorithms are utilized to conduct pulse shape discrimination under raw pulse signals and noise-enhanced datasets. These algorithms include zero-crossing (ZC), charge comparison (CC), falling edge percentage slope (FEPS), frequency gradient analysis (FGA), pulse-coupled neural network (PCNN), ladder gradient (LG), and heterogeneous quasi-continuous spiking cortical model (HQC-SCM). This dataset also provides the source code of all these pulse shape discrimination methods, together with the source code of schematic pulse shape discrimination performance evaluation and anti-noise performance evaluation.