Deep Learning-Based Energy Spectrum Estimation for High Counting Rate Nuclear Spectrometry

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.