AI 中文总结
本研究提出用全卷积神经网络结合R矩阵代码，自动检测中子透射光谱中的共振，虽准确率达93%但泛化能力不足，未来需扩大训练数据集并融入中子共振物理特性以优化模型。
AI 中文摘要
本研究探讨了将传统R矩阵代码与强大的机器学习框架相结合，以自动检测透射光谱中的中子共振的可行性。中子透射数据通常复杂且带有噪声，难以用传统的峰识别方法分析。物理学家目前用于拟合这些数据的最先进R矩阵代码通常依赖先验评估，需要大量人工工作。这项初步研究展示了一种方法，可加速中子透射数据的实验后处理，减少因依赖先验评估带来的偏差。我们采用全卷积神经网络，对7组透射光谱（2组评估数据和5组实验数据）中的单个点进行分类，判断其属于共振区域还是非共振区域。尽管该模型的分类准确率在93%范围内，但进一步分析表明，这一指标夸大了其泛化能力。基于我们在PHYSOR 2026中的前期分析，我们发现，尽管纳入了更多训练数据，该方法仍无法可靠地泛化到未见过的同位素。为解决这些限制，未来工作应评估更大、更多样的训练数据集是否能生成可泛化的模型，并应纳入中子共振的已知物理特性以提升模型性能。
英文摘要
This work investigates the feasibility of augmenting traditional R-Matrix codes with a robust machine learning framework for automatically detecting neutron resonances in transmission spectra. Neutron transmission data are often complex and noisy, making them difficult to analyze using traditional peak-identification methods. The state-of-the-art R-Matrix codes currently used by physicists to fit these data often depend on prior evaluations and require substantial manual effort. This preliminary study demonstrates a method for accelerating the post-experimental processing of neutron transmission data and reducing bias associated with dependence on prior evaluations. We employ a fully convolutional neural network to classify individual points as belonging to resonance or non-resonance regions in seven transmission spectra---two evaluated and five experimental. Although the model achieves classification accuracies in the range of 93\%, further analysis shows that this metric overstates its ability to generalize. Building on our prior analysis in PHYSOR 2026, we find that, despite the inclusion of additional training data, the method does not generalize reliably to previously unseen isotopes. To address these limitations, future work should evaluate whether a larger and more diverse training dataset can produce a generalizable model and should incorporate known physical characteristics of neutron resonances to improve model performance.
Comments29 pages, 11 figures, and 4 tables
