发表机构
Pacific Northwest National Laboratory（太平洋西北国家实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对移动城市搜索中放射性同位素识别的挑战，提出将列表模式伽马数据转换为二维瀑布谱图，并应用计算机视觉架构（MLP、CNN、ViT），其中CNN在低误报率下优于非负矩阵分解方法。
AI 中文摘要
移动城市搜索场景中放射性同位素识别的算法开发面临非均匀背景、瞬时源遭遇以及罕见威胁信号与背景测量之间严重类别不平衡等重大挑战。我们提出了一种基于机器学习的方法，将列表模式伽马射线数据转换为二维瀑布谱图，并将计算机视觉架构应用于生成的图像。我们不将瀑布图视为传统图像，而是采用一种表示方式，其中连续时间谱可以形成输入通道，类似于彩色图像中的RGB通道。这种表示编码了光谱和时间信息，使神经网络能够更有效地学习区分源信号与背景波动的模式。我们在放射性异常检测与识别（RADAI）基准数据集上评估了三种架构：多层感知机（MLP）、卷积神经网络（CNN）和视觉变换器（ViT）。在每小时少于一次误报的假阳性率下，我们的CNN在所有全局指标上均优于先前最佳的非负矩阵分解（NMF）方法，真实检测率、分类率和识别率分别达到0.4334、0.3965和0.2950，而NMF分别为0.4151、0.3611和0.2625。在更低的假阳性率约束下，神经网络方法表现出与NMF相当但最终略低的性能，这表明了进一步研究的机会。
英文摘要
Algorithm development for radioisotope identification in mobile urban search scenarios face significant challenges from non-uniform backgrounds, momentary source encounters, and severe class imbalance between rare threat signatures and background measurements. We present a machine learning-based approach to this problem that converts list-mode gamma-ray data into two-dimensional waterfall spectrograms and applies computer vision architectures to the resulting images. Rather than treating waterfalls as conventional images, we employ a representation where consecutive time spectra can form input channels, similar to RGB channels in color images. This representation encodes both spectral and temporal information, enabling neural networks to more effectively learn patterns that distinguish source signatures from background fluctuations. We evaluate three architectures, a multilayer perceptron (MLP), convolutional neural network (CNN), and vision transformer (ViT), on the Radiological Anomaly Detection and Identification (RADAI) benchmark dataset. At a false positive rate of less than one false alarm per hour, our CNN outperforms the previous-best non-negative matrix factorization (NMF) method across all global metrics, achieving true detection, classification, and identification rates of 0.4334, 0.3965, and 0.2950 respectively, compared to 0.4151, 0.3611, and 0.2625 for NMF. At lower false positive rate constraints, the neural network approaches show comparable but ultimately lower performance than NMF, indicating opportunities for further research.
Comments17 pages, 2 figures, 4 tables
