Neutron resonance cross sections are essential in many nuclear science fields and applications. However, their evaluation and application are extremely complicated. Additionally, the high-frequency, super-wide spectral range of these cross sections cannot be readily approximated by a deep neural network (DNN). To address this issue, we propose a single phase-shift DNN (SPDNN) in which a phase-shift layer is added to a conventional DNN before the output layer to enable wideband processing. Compared with multinetwork algorithms, SPDNN represents a more compact and efficient network, with far fewer parameters. The proposed SPDNN is used to learn the neutron resonance cross sections of 235U fission from the evaluated and experimental libraries, and the results demonstrate its capability as an easy-to-implement, efficient method for approximating the evaluated resonance cross sections and evaluating the experimental data. This study represents the first application of deep learning to the evaluation of highly complex neutron resonance cross sections by adapting DNN.

中文翻译：

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基于深度学习的新型中子共振截面评估


中子共振截面在许多核科学领域和应用中至关重要。然而，它们的评估和应用极其复杂。此外，这些横截面的高频、超宽光谱范围不能轻易地通过深度神经网络（DNN）来近似。为了解决这个问题，我们提出了一种单相移 DNN (SPDNN)，其中在输出层之前将相移层添加到传统 DNN 中以实现宽带处理。与多网络算法相比，SPDNN 代表了更紧凑、更高效的网络，参数也少得多。所提出的 SPDNN 用于从评估库和实验库中学习 235U 裂变的中子共振截面，结果证明其作为一种易于实现、有效的方法来近似评估共振截面和评估实验数据的能力。这项研究代表了深度学习首次应用 DNN 来评估高度复杂的中子共振截面。