Congyu Lin

In nuclear spectroscopy, a physical phenomenon known as the pile-up effect distorts direct measurements by causing temporal overlap of detector pulses. Existing deep learning-based pile-up correction methods rely heavily on supervised training with simulated data, which often generalize poorly to real measurements due to simulation–experiment discrepancies. In this work, we propose a contrastive learning framework to learn robust and transferable representations directly from large-scale unlabeled real nuclear pulse signals. The detector output is segmented into physically complete pulse aggregations using a zero-crossing-based strategy, which serve as semantically coherent instances for representation learning. Physics-inspired data augmentations are designed to realistically model detector noise and bandwidth effects while preserving pulse area. A one-dimensional ResNet encoder is employed for efficient representation learning. The learned representations are transferred to pile-up identification and counting-rate estimation tasks. Experimental results on real nuclear radiation detection systems demonstrate that our method achieves strong performance and robustness under high counting-rate conditions, with particularly pronounced advantages in challenging peak pile-up scenarios.

High counting rates pose a challenging problem for nuclear spectrometric systems, where a physical phenomenon known as the pile-up effect distorts direct measurements, resulting in a significant bias in spectrum estimation. In this paper, we propose an innovative neural network that combines the self-attention mechanism and convolutional neural network (CNN) architectures to address the problem of spectrum estimation. The approach was evaluated on spectral data from a small scintillator (NaI) and simulated signals from a dedicated simulator and compared with conventional methods; furthermore, our method was compared to the state-of-the-art (SOTA) time domain method on the Allpix2 simulator and was evaluated on a real-world dataset. The results demonstrate that the proposed method leads to a more accurate inference of the energy spectrum, even at high count rates. Experiments also show that the proposed method is robust with varying sources, different data scenarios, and noise intensities.