Machine Learning technique for isotopic determination of radioisotopes using HPGe $\mathrm{\gamma}$-ray spectra

TL;DR

This paper explores machine learning regression algorithms for isotopic determination using HPGe gamma-ray spectra, aiming to improve accuracy and efficiency in emergency response scenarios compared to traditional methods.

Contribution

It introduces machine learning techniques as effective alternatives to conventional gamma-ray spectral analysis for isotopic estimation, reducing preprocessing steps and systematic uncertainties.

Findings

Machine learning methods achieve comparable accuracy to traditional techniques.

The approach simplifies analysis by reducing preprocessing steps.

Potential for improved reliability in emergency response situations.

Abstract

$\mathrm{\gamma}$-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 $\mathrm{\gamma}$-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.