Neural Networks for Unfolding Neutron Energy Spectra


Acquiring accurate neutron energy spectrum information is of vital importance to national security as well as personal safety. Unfolding neutron energy spectra from detector responses is a heavily researched area due to the importance of neutron energy for determining radiation dose received. We investigate a new detection system developed by Lawrence Livermore National Laboratory (LLNL), the passive neutron spectrometer (PNS), for use in energy spectrum unfolding techniques primarily in the event of a criticality accident. The unfolded energy spectrum is used to calculate the dose a person would receive in the presence of that neutron field. This detector provides a passive neutron detection method through the use of 55 thermoluminescent dosimeters or gold foils contained within a single polyethylene sphere. Three unfolding algorithms were employed in this research, including our own neural network. 
Doctoral Dissertation advised on the subject:

Publications


[J26] Validation of a passive neutron spectrometer


Zachary T. Condon, Daniel Siefman, Paul Maggi, Paige Witter, Richard Vasques

Accepted for publication in Nuclear Science and Engineering, 2025


[P34] Using machine learning to unfold neutron spectra wth a passive neutron spectrometer


Zachary Condon, Daniel Siefman, Richard Vasques

Proceedings of International Conference on Mathematics & Computational Methods Applied to Nuclear Science & Engineering, Niagara Falls, Canada, 2023 Aug


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