Implementation and testing of a multivariate inverse radiation transport solver.

Area of Science:

• Nuclear Science and Engineering
• Radiation Detection and Measurement
• Applied Physics

Background:

• Special nuclear materials (SNM) detection and characterization rely on analyzing radiation signatures.
• Inverse radiation transport methods are crucial for inferring SNM properties from measured radiation data.
• Accurate SNM identification requires robust methods to interpret complex radiation signatures.

Purpose of the Study:

• To develop and describe a multivariate inverse radiation transport solver for SNM analysis.
• To simultaneously analyze gamma spectrometry and neutron multiplicity measurements.
• To validate the solver's performance using benchmark experiments with plutonium metal.

Main Methods:

• Implementation of a multivariate inverse radiation transport solver.
• Simultaneous analysis of gamma spectrometry and neutron multiplicity data.
• Fitting a one-dimensional radiation transport model with variable layer thicknesses using nonlinear regression.

Main Results:

• The developed solver effectively analyzes combined radiation signatures.
• The method accurately fits radiation transport models to experimental data.
• Performance validation demonstrated successful application to plutonium metal benchmark experiments.

Conclusions:

• The multivariate inverse radiation transport solver offers a powerful tool for SNM detection and characterization.
• Simultaneous analysis of multiple radiation types enhances accuracy in SNM identification.
• The approach is validated and applicable to real-world SNM analysis scenarios.