We found a match
Your institution may have rights to this item. Sign in to continue.
- Title
Use of neural networks to analyze pulse shape data in low-background detectors.
- Authors
Mace, E. K.; Ward, J. D.; Aalseth, C. E.
- 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.
- Subjects
ARTIFICIAL neural networks; DATA analysis; NUCLEAR counters; NUCLEAR chemistry; RADIOACTIVE contamination
- Publication
Journal of Radioanalytical & Nuclear Chemistry, 2018, Vol 318, Issue 1, p117
- ISSN
0236-5731
- Publication type
Article
- DOI
10.1007/s10967-018-5983-1