Improved neutron-gamma discrimination for a 3He neutron detector using subspace learning methods

C. L. Wang, L. L. Funk, R. A. Riedel, K. D. Berry

Research output: Contribution to journalArticlepeer-review

17 Scopus citations

Abstract

3He gas based neutron Linear-Position-Sensitive Detectors (LPSDs) have been used for many neutron scattering instruments. Traditional Pulse-height Analysis (PHA) for Neutron-Gamma Discrimination (NGD) resulted in the neutron-gamma efficiency ratio (NGD ratio) on the order of 105–106. The NGD ratios of 3He detectors need to be improved for even better scientific results from neutron scattering. Digital Signal Processing (DSP) analyses of waveforms were proposed for obtaining better NGD ratios, based on features extracted from rise-time, pulse amplitude, charge integration, a simplified Wiener filter, and the cross-correlation between individual and template waveforms of neutron and gamma events. Fisher Linear Discriminant Analysis (FLDA) and three Multivariate Analyses (MVAs) of the features were performed. The NGD ratios are improved by about 102–103 times compared with the traditional PHA method. Our results indicate the NGD capabilities of 3He tube detectors can be significantly improved with subspace-learning based methods, which may result in a reduced data-collection time and better data quality for further data reduction.

Keywords

  • Digital signal processing
  • He-3 neutron detectors
  • Neutron-gamma discrimination
  • Subspace learning methods

Fingerprint

Dive into the research topics of 'Improved neutron-gamma discrimination for a 3He neutron detector using subspace learning methods'. Together they form a unique fingerprint.

Cite this