Recovery of Saturated $\gamma$ Signal Waveforms by Artificial Neural Networks

Autor:	Liu, Yu, Zhu, Jing-Jun, Roberts, Neil, Chen, Ke-Ming, Yan, Yu-Lu, Mo, Shuang-Rong, Gu, Peng, Xing, Hao-Yang
Rok vydání:	2018
Předmět:	Physics - Data Analysis Statistics and Probability High Energy Physics - Experiment Nuclear Experiment
Druh dokumentu:	Working Paper
Popis:	Particle may sometimes have energy outside the range of radiation detection hardware so that the signal is saturated and useful information is lost. We have therefore investigated the possibility of using an Artificial Neural Network (ANN) to restore the saturated waveforms of $\gamma$ signals. Several ANNs were tested, namely the Back Propagation (BP), Simple Recurrent (Elman), Radical Basis Function (RBF) and Generalized Radial Basis Function (GRBF) neural networks (NNs) and compared with the fitting method based on the Marrone model. The GBRFNN was found to perform best. Comment: 8 pages, 8 figures, Preprint submitted to Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1810.08200 Zobrazit plný text záznamu View this record from Arxiv