Transformer-Based Pulse Shape Discrimination in HPGe Detectors with Masked Autoencoder Pre-training
This paper demonstrates that transformer-based models, particularly when enhanced by masked autoencoder pre-training, outperform traditional gradient-boosted decision trees in pulse-shape discrimination and energy regression for HPGe detectors by leveraging full waveform data and significantly reducing the need for labeled training samples.