A Hierarchical Bayesian Approach to Neutron Spectrum Unfolding with Organic Scintillators

TL;DR

This paper introduces a hierarchical Bayesian model with Monte Carlo sampling to accurately and robustly estimate neutron spectra from organic scintillator data, providing uncertainty quantification and outperforming existing methods.

Contribution

The paper presents a novel hierarchical Bayesian approach for neutron spectrum unfolding that improves accuracy, robustness, and uncertainty quantification over traditional methods.

Findings

Comparable or better accuracy than existing methods

Robust performance with limited detection events

Provides posterior confidence measures

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 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 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 ($^{252}$Cf and $^{241}$AmBe). 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.