19–23 Jun 2023
Hofburg Palace & Online
Europe/Vienna timezone

Neural-Network Based Isotope Estimation with Simultaneous Curve Fitting

21 Jun 2023, 11:00


Board: 51
E-poster T3.6 Analysis of Radionuclide Monitoring Data Lightning talks: P2.2, P3.2, P3.6


Dr J.R. Powers-Luhn (Pacific Northwest National Laboratory (PNNL))


Radioxenon is used to identify underground nuclear explosions by quantifying the amount and isotopic ratios of Xe-135, Xe-133, Xe-133m, and Xe-131m. Determining these concentrations requires knowledge of the detector performing the measurement and the accurate attribution of each measured decay. The current standard for estimating the activity concentration employs a beta/gamma coincidence histogram combined with 7 or 10 rectangular regions-of-interest (ROI). These ROI are specific to the detector type, based on the energy resolution and efficiency of the detector. We present an alternative method of count attribution which employs neural networks trained on simulated data to generate probabilistic assignment for each detected count. These networks are physics-informed and employ gaussian curve fitting to closely model the expected behavior of radiation detectors. This reduces the parameters (hundreds compared to hundreds of thousands in related work) and maintains explainability of the results. Our work demonstrates a method of incorporating machine learning into radioisotope detection that does not require faith in a black-box model.

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Application of physics-informed machine learning to the estimation of radionuclide concentration in noble gas detectors while preserving explainability.

E-mail [email protected]

Primary author

Dr J.R. Powers-Luhn (Pacific Northwest National Laboratory (PNNL))


Mr Matthew Cooper (Pacific Northwest National Laboratory (PNNL)) Mr Michael Mayer (Pacific Northwest National Laboratory (PNNL))

Presentation materials