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

TL;DR

This paper explores using various artificial neural networks to restore saturated gamma signal waveforms, demonstrating that the GBRFNN outperforms other models and traditional fitting methods in recovering lost information.

Contribution

The study introduces a neural network approach, particularly the GBRFNN, for effectively restoring saturated gamma waveforms, surpassing existing fitting techniques.

Findings

GBRFNN achieved the best performance among tested models.

Neural networks outperform traditional fitting methods in waveform restoration.

Restoration accuracy improves with neural network-based approaches.

Abstract

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.