Gamma-ray spectroscopy is essential in nuclear science, enabling the identification of radioactive materials through energy spectrum analysis. However, high count rates lead to pile-up effects, resulting in spectral distortions that hinder accurate isotope identification and activity estimation. This phenomenon highlights the need for automated and precise approaches to pile-up correction. We propose a novel deep learning (DL) framework plugging count rate information of pile-up signals with a 2D attention U-Net for energy spectrum recovery. The input to the model is an Energy–Duration matrix constructed from preprocessed pulse signals. Temporal and spatial features are jointly extracted, with count rate information embedded to enhance robustness under high count rate conditions. Training data were generated using an open-source simulator based on a public gamma spectrum database. The model’s performance was evaluated using Kullback–Leibler (KL) divergence, Mean Squared Error (MSE) Energy Resolution (ER), and Full Width at Half Maximum (FWHM). Results indicate that the proposed framework effectively predicts accurate spectra, minimizing errors even under severe pile-up effects. This work provides a robust framework for addressing pile-up effects in gamma-ray spectroscopy, presenting a practical solution for automated, high-accuracy spectrum estimation. The integration of temporal and spatial learning techniques offers promising prospects for advancing high-activity nuclear analysis applications.

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.