A Hierarchical Bayesian Approach to Neutron Spectrum Unfolding with Organic Scintillators

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

Research output: Contribution to journalArticlepeer-review


We propose a hierarchical Bayesian model and a state-of-the-art Monte Carlo sampling method to solve the unfolding problem, i.e., to estimate the spectrum of an unknown neutron 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 the 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 (energywise) continuous neutron sources (252Cf and 241AmBe). Our results show that the proposed method has similar or better unfolding performance compared with 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 (US)
Article number8836651
Pages (from-to)2265-2274
Number of pages10
JournalIEEE Transactions on Nuclear Science
Issue number10
StatePublished - Oct 2019


  • Bayesian inference
  • markov chain Monte Carlo (MCMC) methods
  • organic scintillators
  • spectral unfolding

ASJC Scopus subject areas

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


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