Speaker
Description
Reliable neutron source identification is essential for nuclear nonproliferation, safeguards, and homeland security, yet remains challenging due to the ill-conditioned nature of neutron spectral inversion. Here, we present a scalable Bayesian framework that overcomes these limitations through evidence-based model selection. We introduce a Bayesian Evidence Adaptive Pursuit (ABEP) algorithm that efficiently explores the combinatorial space of possible source ensembles by iteratively ranking and pruning candidate models using Bayesian evidence. We benchmark the framework using both experimental measurements and synthetic datasets generated with high-fidelity Monte Carlo simulations. The results demonstrate accurate identification of both single- and multi-source ensembles with high statistical significance ($>\!3\sigma$) and favorable scaling with detected event counts ($\sim\!\!10^3$ for single-source identification). These findings establish ABEP as a practical and robust tool for neutron source identification and significantly extend the operational capabilities of scatter-based neutron spectrometers in nuclear security and safeguards applications.
| Minioral | No |
|---|---|
| IEEE Member | Yes |
| Are you a student? | No |