Enhancing radioisotope identification in gamma spectra via supervised domain adaptation

Abstract

Machine learning methods in gamma spectroscopy have the potential to provide accurate, real-time classification of unknown radioactive samples. However, obtaining sufficient experimental training data is often prohibitively expensive and time-consuming, and models trained solely on simulated data can struggle to generalize to the unpredictable range of real-world operating scenarios. In this study, we explore how supervised domain adaptation techniques can improve radioisotope identification models by transferring knowledge between different data domains. We begin by pretraining a model for radioisotope identification using data from a synthetic source domain, and then fine-tune it for a new target domain that shares the same label space. Our analysis indicates that fine-tuned models significantly outperform those trained exclusively on source-domain data or solely on target-domain data, particularly in the intermediate data regime ( 10^2 to 10^5 target training samples). This conclusion is consistent across four different machine learning architectures (MLP, CNN, Transformer, and LSTM). Furthermore, our findings show that fine-tuned Transformers yield a statistically significant improvement in test performance compared to the other architectures. Overall, this study serves as a proof of concept for applying supervised domain adaptation techniques to gamma spectroscopy in scenarios where experimental data is limited.

Tasks

Reproductions