Title: Towards energy-insensitive and robust neutron/gamma classification: A learning-based frequency-domain parametric approach Show Chinese title

Title: 面向能量不敏感且鲁棒的中子/伽马分类：一种基于学习的频域参数化方法

Abstract: Neutron/gamma discrimination has been intensively researched in recent years, due to its unique scientific value and widespread applications. With the advancement of detection materials and algorithms, nowadays we can achieve fairly good discrimination. However, further improvements rely on better utilization of detector raw signals, especially energy-independent pulse characteristics. We begin by discussing why figure-of-merit (FoM) is not a comprehensive criterion for high-precision neutron/gamma discriminators, and proposing a new evaluation method based on adversarial sampling. Inspired by frequency-domain analysis in existing literature, parametric linear/nonlinear models with minimum complexity are created, upon the discrete spectrum, with tunable parameters just as neural networks. We train the models on an open-source neutron/gamma dataset (CLYC crystals with silicon photomultipliers) preprocessed by charge normalization to discover and exploit energy-independent features. The performance is evaluated on different sampling rates and noise levels, in comparison with the frequency classification index and conventional methods. The frequency-domain parametric models show higher accuracy and better adaptability to variations of data integrity than other discriminators. The proposed method is also promising for online inference on economical hardware and portable devices. Show Chinese abstract

Abstract: 中子/伽马鉴别近年来受到了广泛研究，因其独特的科学价值和广泛的应用。随着检测材料和算法的进步，如今我们可以实现相当好的鉴别效果。然而，进一步的改进依赖于对探测器原始信号的更好利用，尤其是能量无关的脉冲特征。我们首先讨论为什么优劣指标（FoM）不是高精度中子/伽马鉴别器的全面标准，并提出一种基于对抗采样的新评估方法。受现有文献中频域分析的启发，创建了具有最小复杂度的参数化线性/非线性模型，在离散谱上，可调参数就像神经网络一样。我们在一个开源的中子/伽马数据集（CLYC晶体与硅光电倍增管）上训练这些模型，该数据集经过电荷归一化预处理，以发现和利用能量无关的特征。在不同的采样率和噪声水平下，与其他频率分类指数和传统方法相比，评估其性能。频域参数化模型比其他鉴别器具有更高的准确性和更好的数据完整性变化适应性。所提出的方法在经济硬件和便携设备上的在线推理也具有前景。