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 synthetic data can struggle to generalize to the unpredictable range of real-world operating scenarios. In this work, we pretrain a model using physically derived synthetic data and subsequently leverage transfer learning techniques to fine-tune the model for a specific target domain. This paradigm enables us to embed physical principles during the pretraining step, thus requiring less data from the target domain compared to classical machine learning methods. Results of this analysis indicate that fine-tuned models significantly outperform those trained exclusively on synthetic data or solely on target-domain data, particularly in the intermediate data regime (${\approx} 10^4$ training samples). This conclusion is consistent across four different machine learning architectures (MLP, CNN, Transformer, and LSTM) considered in this study. This research serves as proof of concept for applying transfer learning techniques to application scenarios where access to experimental data is limited.

Officials are turning to'talking' drones to keep citizens safe amid the coronavirus outbreak. Get all the latest news on coronavirus and more delivered daily to your inbox. DAYTONA BEACH, Fla. -- Kim Andrade was just walking along Daytona beach when a drone came buzzing by. It caught her attention, and she even went ahead and snapped a few photos. What she did not expect to learn, however, is that the drone has a voice.