A Hierarchical Bayesian Approach to Neutron Spectrum Unfolding with Organic Scintillators

Haonan Zhu, Yoann Altmann, Angela Di Fulvio, Stephen McLaughlin, Sara Pozzi, Alfred O. Hero

Research output: Contribution to journalArticle

Abstract

We propose a hierarchical Bayesian model and state- of-art Monte Carlo sampling method to solve the unfolding problem, i.e., to estimate the spectrum of an unknown neu- tron source from the data detected by an organic scintillator. Inferring neutron spectra is important for several applications, including nonproliferation and nuclear security, as it allows the discrimination of fission sources in special nuclear material (SNM) from other types of neutron sources based on the differences of the emitted neutron spectra. Organic scintillators interact with neutrons mostly via elastic scattering on hydrogen nuclei and therefore partially retain neutron energy information. Consequently, the neutron spectrum can be derived through deconvolution of the measured light output spectrum and the response functions of the scintillator to monoenergetic neutrons. The proposed approach is compared to three existing methods using simulated data to enable controlled benchmarks. We consider three sets of detector responses. One set corresponds to a 2.5 MeV monoenergetic neutron source and two sets are associated with (energy-wise) continuous neutron sources (252Cf and 241AmBe). Our results show that the proposed method has similar or better unfolding performance compared to other iterative or Tikhonov regularization-based approaches in terms of accuracy and robustness against limited detection events, while requiring less user supervision. The proposed method also provides a posteriori confidence measures, which offers additional information regarding the uncertainty of the measurements and the extracted information.
Original languageEnglish
JournalIEEE Transactions on Nuclear Science
Publication statusAccepted/In press - 9 Sep 2019

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neutron spectra
neutron sources
scintillation counters
neutrons
fission
discrimination
confidence
elastic scattering
sampling
nuclei
energy
output
detectors
hydrogen
estimates

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title = "A Hierarchical Bayesian Approach to Neutron Spectrum Unfolding with Organic Scintillators",
abstract = "We propose a hierarchical Bayesian model and state- of-art Monte Carlo sampling method to solve the unfolding problem, i.e., to estimate the spectrum of an unknown neu- tron source from the data detected by an organic scintillator. Inferring neutron spectra is important for several applications, including nonproliferation and nuclear security, as it allows the discrimination of fission sources in special nuclear material (SNM) from other types of neutron sources based on the differences of the emitted neutron spectra. Organic scintillators interact with neutrons mostly via elastic scattering on hydrogen nuclei and therefore partially retain neutron energy information. Consequently, the neutron spectrum can be derived through deconvolution of the measured light output spectrum and the response functions of the scintillator to monoenergetic neutrons. The proposed approach is compared to three existing methods using simulated data to enable controlled benchmarks. We consider three sets of detector responses. One set corresponds to a 2.5 MeV monoenergetic neutron source and two sets are associated with (energy-wise) continuous neutron sources (252Cf and 241AmBe). Our results show that the proposed method has similar or better unfolding performance compared to other iterative or Tikhonov regularization-based approaches in terms of accuracy and robustness against limited detection events, while requiring less user supervision. The proposed method also provides a posteriori confidence measures, which offers additional information regarding the uncertainty of the measurements and the extracted information.",
author = "Haonan Zhu and Yoann Altmann and {Di Fulvio}, Angela and Stephen McLaughlin and Sara Pozzi and Hero, {Alfred O.}",
year = "2019",
month = "9",
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language = "English",
journal = "IEEE Transactions on Nuclear Science",
issn = "0018-9499",
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A Hierarchical Bayesian Approach to Neutron Spectrum Unfolding with Organic Scintillators. / Zhu, Haonan; Altmann, Yoann; Di Fulvio, Angela; McLaughlin, Stephen; Pozzi, Sara; Hero, Alfred O.

In: IEEE Transactions on Nuclear Science, 09.09.2019.

Research output: Contribution to journalArticle

TY - JOUR

T1 - A Hierarchical Bayesian Approach to Neutron Spectrum Unfolding with Organic Scintillators

AU - Zhu, Haonan

AU - Altmann, Yoann

AU - Di Fulvio, Angela

AU - McLaughlin, Stephen

AU - Pozzi, Sara

AU - Hero, Alfred O.

PY - 2019/9/9

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N2 - We propose a hierarchical Bayesian model and state- of-art Monte Carlo sampling method to solve the unfolding problem, i.e., to estimate the spectrum of an unknown neu- tron source from the data detected by an organic scintillator. Inferring neutron spectra is important for several applications, including nonproliferation and nuclear security, as it allows the discrimination of fission sources in special nuclear material (SNM) from other types of neutron sources based on the differences of the emitted neutron spectra. Organic scintillators interact with neutrons mostly via elastic scattering on hydrogen nuclei and therefore partially retain neutron energy information. Consequently, the neutron spectrum can be derived through deconvolution of the measured light output spectrum and the response functions of the scintillator to monoenergetic neutrons. The proposed approach is compared to three existing methods using simulated data to enable controlled benchmarks. We consider three sets of detector responses. One set corresponds to a 2.5 MeV monoenergetic neutron source and two sets are associated with (energy-wise) continuous neutron sources (252Cf and 241AmBe). Our results show that the proposed method has similar or better unfolding performance compared to other iterative or Tikhonov regularization-based approaches in terms of accuracy and robustness against limited detection events, while requiring less user supervision. The proposed method also provides a posteriori confidence measures, which offers additional information regarding the uncertainty of the measurements and the extracted information.

AB - We propose a hierarchical Bayesian model and state- of-art Monte Carlo sampling method to solve the unfolding problem, i.e., to estimate the spectrum of an unknown neu- tron source from the data detected by an organic scintillator. Inferring neutron spectra is important for several applications, including nonproliferation and nuclear security, as it allows the discrimination of fission sources in special nuclear material (SNM) from other types of neutron sources based on the differences of the emitted neutron spectra. Organic scintillators interact with neutrons mostly via elastic scattering on hydrogen nuclei and therefore partially retain neutron energy information. Consequently, the neutron spectrum can be derived through deconvolution of the measured light output spectrum and the response functions of the scintillator to monoenergetic neutrons. The proposed approach is compared to three existing methods using simulated data to enable controlled benchmarks. We consider three sets of detector responses. One set corresponds to a 2.5 MeV monoenergetic neutron source and two sets are associated with (energy-wise) continuous neutron sources (252Cf and 241AmBe). Our results show that the proposed method has similar or better unfolding performance compared to other iterative or Tikhonov regularization-based approaches in terms of accuracy and robustness against limited detection events, while requiring less user supervision. The proposed method also provides a posteriori confidence measures, which offers additional information regarding the uncertainty of the measurements and the extracted information.

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