Abstract Recent research has focused on the development of surrogate models for radiation source localization in a simulated urban domain. We employ the Monte Carlo N-Particle (MCNP) code to provide high-fidelity simulations of radiation transport within an urban domain. The model is constructed to employ a source location ( ) as input and return the estimated count rate for a set of specified detector locations. Because MCNP simulations are computationally expensive, we develop efficient and accurate surrogate models of the detector responses. We construct surrogate models using Gaussian processes and neural networks that we train and verify using the MCNP simulations. The trained surrogate models provide an efficient framework for Bayesian inference and experimental design. We employ Delayed Rejection Adaptive Metropolis (DRAM), a Markov Chain Monte Carlo algorithm, to infer the location and intensity of an unknown source. The DRAM results yield a posterior probability distribution for the source’s location conditioned on the observed detector count rates. The posterior distribution exhibits regions of high and low probability within the simulated environment identifying potential source locations. In this manner, we can quantify the source location to within at least one of these regions of high probability in the considered cases. Employing these methods, we are able to reduce the space of potential source locations by at least 60%.

中文翻译：

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使用从 3-D 蒙特卡罗传输物理模拟构建的替代模型进行辐射源定位

摘要 最近的研究集中在模拟城市域中辐射源定位的替代模型的开发。我们采用蒙特卡罗 N 粒子 (MCNP) 代码来提供城市域内辐射传输的高保真模拟。该模型被构建为采用源位置 ( ) 作为输入并返回一组指定探测器位置的估计计数率。由于 MCNP 模拟的计算成本很高，因此我们开发了检测器响应的高效且准确的替代模型。我们使用高斯过程和神经网络构建代理模型，我们使用 MCNP 模拟训练和验证这些模型。经过训练的代理模型为贝叶斯推理和实验设计提供了一个有效的框架。我们采用延迟拒绝自适应大都会（DRAM），马尔可夫链蒙特卡罗算法，用于推断未知源的位置和强度。DRAM 结果产生源位置的后验概率分布，条件是观察到的探测器计数率。后验分布在模拟环境中显示出高概率和低概率的区域，以确定潜在的源位置。以这种方式，我们可以在所考虑的情况下将源位置量化到这些高概率区域中的至少一个内。使用这些方法，我们能够将潜在源位置的空间减少至少 60%。后验分布在模拟环境中显示出高概率和低概率的区域，以确定潜在的源位置。以这种方式，我们可以在所考虑的情况下将源位置量化到这些高概率区域中的至少一个内。使用这些方法，我们能够将潜在源位置的空间减少至少 60%。后验分布在模拟环境中显示出高概率和低概率的区域，以确定潜在的源位置。通过这种方式，我们可以在所考虑的情况下将源位置量化到这些高概率区域中的至少一个区域内。使用这些方法，我们能够将潜在源位置的空间减少至少 60%。