Machine Learning technique for isotopic determination of radioisotopes using HPGe $\mathrmγ$-ray spectra
This work addresses challenges in isotopic determination for emergency response, though it appears incremental as it applies existing ML methods to a specific domain.
The paper tackled the problem of isotopic determination from gamma-ray spectra by applying machine learning regression algorithms as alternatives to traditional methods, showing comparable performance in emergency response applications while reducing systematic uncertainty by eliminating analysis steps.
$\mathrmγ$-ray spectroscopy is a quantitative, non-destructive technique that may be utilized for the identification and quantitative isotopic estimation of radionuclides. Traditional methods of isotopic determination have various challenges that contribute to statistical and systematic uncertainties in the estimated isotopics. Furthermore, these methods typically require numerous pre-processing steps, and have only been rigorously tested in laboratory settings with limited shielding. In this work, we examine the application of a number of machine learning based regression algorithms as alternatives to conventional approaches for analyzing $\mathrmγ$-ray spectroscopy data in the Emergency Response arena. This approach not only eliminates many steps in the analysis procedure, and therefore offers potential to reduce this source of systematic uncertainty, but is also shown to offer comparable performance to conventional approaches in the Emergency Response Application.