  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.

Instrumentation and Detectors;Nuclear Experiment;Computational Physics;Applications