This paper addresses the problem of detecting the presence and location of a small low emission source inside an object, when the background noise dominates. This problem arises, for instance, in some homeland security applications. The goal is to reach the signal-to-noise ratio levels in the order of 10(-3). A Bayesian approach to this problem is implemented in 2D. The method allows inference not only about the existence of the source, but also about its location. We derive Bayes factors for model selection and estimation of location based on Markov chain Monte Carlo simulation. A simulation study shows that with sufficiently high total emission level, our method can effectively locate the source.

中文翻译：

---

检测小型低排放源的贝叶斯方法

本文解决了当背景噪声占主导地位时检测物体内部小型低发射源的存在和位置的问题。例如，在某些国土安全应用中会出现此问题。目标是达到 10(-3) 数量级的信噪比水平。对这个问题的贝叶斯方法是在 2D 中实现的。该方法不仅可以推断源的存在，还可以推断其位置。我们基于马尔可夫链蒙特卡罗模拟推导出用于模型选择和位置估计的贝叶斯因子。模拟研究表明，在总排放水平足够高的情况下，我们的方法可以有效地定位源。