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


Conference paper


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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APA   Click to copy
Condon, Z., Siefman, D., & Vasques, R. (2023). [P34] Using machine learning to unfold neutron spectra wth a passive neutron spectrometer. In Proceedings of International Conference on Mathematics & Computational Methods Applied to Nuclear Science & Engineering, Niagara Falls, Canada.


Chicago/Turabian   Click to copy
Condon, Zachary, Daniel Siefman, and Richard Vasques. “[P34] Using Machine Learning to Unfold Neutron Spectra Wth a Passive Neutron Spectrometer.” In Proceedings of International Conference on Mathematics &Amp; Computational Methods Applied to Nuclear Science &Amp; Engineering, Niagara Falls, Canada, 2023.


MLA   Click to copy
Condon, Zachary, et al. “[P34] Using Machine Learning to Unfold Neutron Spectra Wth a Passive Neutron Spectrometer.” Proceedings of International Conference on Mathematics &Amp; Computational Methods Applied to Nuclear Science &Amp; Engineering, Niagara Falls, Canada, 2023.


BibTeX   Click to copy

@inproceedings{zachary2023a,
  title = {[P34] Using machine learning to unfold neutron spectra wth a passive neutron spectrometer},
  year = {2023},
  month = aug,
  journal = {Proceedings of International Conference on Mathematics & Computational Methods Applied to Nuclear Science & Engineering, Niagara Falls, Canada},
  author = {Condon, Zachary and Siefman, Daniel and Vasques, Richard},
  month_numeric = {8}
}

ABSTRACT:  Unfolding neutron spectra is a heavily researched area due to the importance of neutron energy for determining radiation dose received. A novel detection system, the passive neutron spectrometer (PNS), is being investigated for use in energy spectrum unfolding techniques. The benefit of this detector is the passive detection of neutrons through the use of 55 thermoluminescent dosimeters or gold foils contained within a single polyethylene sphere. Multiple real-world detector responses were unfolded using the well-established MAXED algorithm as well as a novel neural network technique.

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