Discrimination of Neutron-Gamma Ray Pulses with Pileup using Normalized Cross Correlation and Principal Component Analysis

Arindam Dutta, Keith Holbert

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

During high event rates, discrimination of neutron and gamma-ray pulses can be challenging because of pulse pileup. In this paper, we develop a novel approach to deal with this problem. In our method, the normalized cross correlation (NCC) is used to characterize the behavior of typical neutron and gamma-ray pulses. The gradient of the NCC curve is shown to provide distinct features that can be used to distinguish neutron from gamma-ray pulses. Principal component analysis (PCA) is employed to extract features from the NCC gradient curve. We have employed the standard PCA approach and a modified PCA version to obtain unique features. The modified PCA method first extracts 20 Kolmogorov-Smirnov points and then computes the principal components of these 20 coefficients. We have exercised the technique on both simulated (for different pileup delays) and measurement data from a CLYC detector (for varying event rates). The modified PCA approach shows more promising results than the standard PCA approach with better figure of merit.

Original languageEnglish (US)
Article number7582529
JournalIEEE Transactions on Nuclear Science
VolumePP
Issue number99
DOIs
StatePublished - 2016

Fingerprint

principal components analysis
Gamma rays
Principal component analysis
cross correlation
discrimination
Neutrons
gamma rays
neutrons
pulses
gradients
curves
figure of merit
Detectors
detectors
coefficients

Keywords

  • CLYC
  • FOM
  • gamma ray
  • K.S.
  • KS test
  • neutron
  • PCA
  • pulse pileup
  • pulse shape discrimination

ASJC Scopus subject areas

  • Nuclear and High Energy Physics
  • Nuclear Energy and Engineering
  • Electrical and Electronic Engineering

Cite this

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title = "Discrimination of Neutron-Gamma Ray Pulses with Pileup using Normalized Cross Correlation and Principal Component Analysis",
abstract = "During high event rates, discrimination of neutron and gamma-ray pulses can be challenging because of pulse pileup. In this paper, we develop a novel approach to deal with this problem. In our method, the normalized cross correlation (NCC) is used to characterize the behavior of typical neutron and gamma-ray pulses. The gradient of the NCC curve is shown to provide distinct features that can be used to distinguish neutron from gamma-ray pulses. Principal component analysis (PCA) is employed to extract features from the NCC gradient curve. We have employed the standard PCA approach and a modified PCA version to obtain unique features. The modified PCA method first extracts 20 Kolmogorov-Smirnov points and then computes the principal components of these 20 coefficients. We have exercised the technique on both simulated (for different pileup delays) and measurement data from a CLYC detector (for varying event rates). The modified PCA approach shows more promising results than the standard PCA approach with better figure of merit.",
keywords = "CLYC, FOM, gamma ray, K.S., KS test, neutron, PCA, pulse pileup, pulse shape discrimination",
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T1 - Discrimination of Neutron-Gamma Ray Pulses with Pileup using Normalized Cross Correlation and Principal Component Analysis

AU - Dutta, Arindam

AU - Holbert, Keith

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N2 - During high event rates, discrimination of neutron and gamma-ray pulses can be challenging because of pulse pileup. In this paper, we develop a novel approach to deal with this problem. In our method, the normalized cross correlation (NCC) is used to characterize the behavior of typical neutron and gamma-ray pulses. The gradient of the NCC curve is shown to provide distinct features that can be used to distinguish neutron from gamma-ray pulses. Principal component analysis (PCA) is employed to extract features from the NCC gradient curve. We have employed the standard PCA approach and a modified PCA version to obtain unique features. The modified PCA method first extracts 20 Kolmogorov-Smirnov points and then computes the principal components of these 20 coefficients. We have exercised the technique on both simulated (for different pileup delays) and measurement data from a CLYC detector (for varying event rates). The modified PCA approach shows more promising results than the standard PCA approach with better figure of merit.

AB - During high event rates, discrimination of neutron and gamma-ray pulses can be challenging because of pulse pileup. In this paper, we develop a novel approach to deal with this problem. In our method, the normalized cross correlation (NCC) is used to characterize the behavior of typical neutron and gamma-ray pulses. The gradient of the NCC curve is shown to provide distinct features that can be used to distinguish neutron from gamma-ray pulses. Principal component analysis (PCA) is employed to extract features from the NCC gradient curve. We have employed the standard PCA approach and a modified PCA version to obtain unique features. The modified PCA method first extracts 20 Kolmogorov-Smirnov points and then computes the principal components of these 20 coefficients. We have exercised the technique on both simulated (for different pileup delays) and measurement data from a CLYC detector (for varying event rates). The modified PCA approach shows more promising results than the standard PCA approach with better figure of merit.

KW - CLYC

KW - FOM

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KW - pulse pileup

KW - pulse shape discrimination

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