# Machine Learning technique for isotopic determination of radioisotopes using HPGe γ-ray spectra

arXiv: https://arxiv.org/abs/2301.01415 · HTML: https://mlsanity.com/paper/2301.01415/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 64 references that are in the map.
- First posted on arXiv 4 Jan 2023.

## Builds on (most cited first, 2 of 2)

- [Deep Learning in Neural Networks: An Overview](https://mlsanity.com/paper/1404.7828/) (2014)
- [A Random Forest Guided Tour](https://mlsanity.com/paper/1511.05741/) (2015)

## Built on by (most cited first, 3 of 3)

- [Sim-to-real supervised domain adaptation for radioisotope identification](https://mlsanity.com/paper/2412.07069/) (2024)
- [Material Identification using Multi-Modal Intrinsic Radiation and Radiography](https://mlsanity.com/paper/2603.27036/) (2026)
- [Peak-Based Nuclide Identification in HPGe γ-Spectrometry with Machine Learning and SHAP](https://mlsanity.com/paper/2606.14874/) (2026)

## Abstract (arXiv)

γ-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 γ-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.