Use of neural networks to analyze pulse shape data in low-background detectors

Abstract

Pacific Northwest National Laboratory has accumulated years of data with ultra-low-background proportional counters collected in an on-site shallow underground laboratory. This large dataset of events is exploited to study the impact of using neural networks for data analysis compared to simple pulse shape discrimination (PSD). The PSD method can introduce false positives for overlapping event distributions; however, a neural network can separate and correctly classify these events. This paper describes the training, testing, and validation of a neural network, analysis of challenge datasets, and a comparison between the standard PSD approach and a dense, fully-connected neural network.