This work details the generation of synthetic radiation data using large-scale Monte Carlo transport models to evaluate radiation search detection algorithms. Modular 3-D Monte Carlo models spanning multiple city blocks were constructed, loosely based on downtown Knoxville, TN, containing buildings composed of multiple materials (brick, granite, and concrete), sidewalks, a four-lane road, side streets, parking lots, and grassy fields. Background and simulated source detector response calculations from these models were used to create synthetic list mode data sets for a $2\,\,^{\prime \prime } \times 4\,\,^{\prime \prime } \times 16\,\,^{\prime \prime }$ NaI(Tl) detector moving through a city street at a constant speed. For the background simulations, major isotopes were computed individually so that background composition and variability could be computed efficiently outside Monte Carlo. The source detector response included six simulated sources placed at 15 source locations. Detector response was developed to be periodic through the city street so that a detector path could begin at one end of the model and wrap around to the other. This framework allowed for the creation of diverse data sets, each with its own unique background and simulated source detector response. Synthetic data allows for high-quality labels, which are useful in developing data-driven radiation detection algorithms. This methodology was used to create synthetic data sets which were released as part of a public data competition to spur the development of new radiation detection algorithms for radiological search applications.

中文翻译：

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为辐射检测算法竞赛生成合成数据

这项工作详细介绍了使用大规模蒙特卡罗传输模型来评估辐射搜索检测算法的合成辐射数据的生成。构建了跨越多个城市街区的模块化 3-D 蒙特卡罗模型，松散地基于田纳西州诺克斯维尔市中心，包含由多种材料（砖、花岗岩和混凝土）、人行道、四车道道路、小巷、停车场组成的建筑物，和草地。这些模型的背景和模拟源探测器响应计算用于为 $2\,\,^{\prime \prime } \times 4\,\,^{\prime \prime } \times 16 创建合成列表模式数据集\,\,^{\prime \prime }$ NaI(Tl) 探测器以恒定速度穿过城市街道。对于背景模拟，主要同位素是单独计算的，因此可以在蒙特卡罗之外有效地计算背景成分和变异性。源探测器响应包括放置在 15 个源位置的六个模拟源。探测器响应被开发为在城市街道上具有周期性，因此探测器路径可以从模型的一端开始并环绕到另一端。该框架允许创建不同的数据集，每个数据集都有自己独特的背景和模拟的源检测器响应。合成数据允许使用高质量标签，这对于开发数据驱动的辐射检测算法非常有用。