Machine learning for digital pulse shape discrimination

T. S. Sanderson, C. D. Scott, M. Flaska, J. K. Polack, S. A. Pozzi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

15 Scopus citations

Abstract

Accurate discrimination of neutrons and gamma rays is critical for organic scintillation detectors, especially for detection systems where minimal false-alarm rates are paramount (nuclear non-proliferation). Poor pulse shape discrimination (PSD) necessitates long measurement times, and may also cause inaccurate characterization of emitted neutrons, leading to source misidentification. Digital, data-acquisition, measurement systems using a charge-integration PSD method are commonly used for particle classification. A 2-D, charge-integration PSD method tends to be reasonably accurate, although the separation is typically poor at lower energies (below ∼ 500-keV neutron energy deposited). The charge-integration method originated in analog systems; however, with digital measurement systems there is no need to restrict to only two features (for instance, tail and total integrals) of the pulse. Instead, a classifier may be a much more complex function of the measured pulse. In this work, we apply a machine-learning methodology; namely, the support vector machine (SVM), to determine a PSD classifier. We show that the SVM method leads to improved detection performance with respect to the charge-integration method. We also apply a recently developed methodology that gives more accurate performance estimates by accounting for the fact that the training data needed for the SVM are 'contaminated'.

Original languageEnglish (US)
Title of host publication2012 IEEE Nuclear Science Symposium and Medical Imaging Conference Record, NSS/MIC 2012
Pages199-202
Number of pages4
DOIs
StatePublished - Dec 1 2012
Event2012 IEEE Nuclear Science Symposium and Medical Imaging Conference Record, NSS/MIC 2012 - Anaheim, CA, United States
Duration: Oct 29 2012Nov 3 2012

Publication series

NameIEEE Nuclear Science Symposium Conference Record
ISSN (Print)1095-7863

Other

Other2012 IEEE Nuclear Science Symposium and Medical Imaging Conference Record, NSS/MIC 2012
CountryUnited States
CityAnaheim, CA
Period10/29/1211/3/12

All Science Journal Classification (ASJC) codes

  • Radiation
  • Nuclear and High Energy Physics
  • Radiology Nuclear Medicine and imaging

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    Sanderson, T. S., Scott, C. D., Flaska, M., Polack, J. K., & Pozzi, S. A. (2012). Machine learning for digital pulse shape discrimination. In 2012 IEEE Nuclear Science Symposium and Medical Imaging Conference Record, NSS/MIC 2012 (pp. 199-202). [6551092] (IEEE Nuclear Science Symposium Conference Record). https://doi.org/10.1109/NSSMIC.2012.6551092