Alamaniotis Miltiadis
Department of Electrical and Computer Engineering, University of Texas at San Antonio, San Antonio, TX, 78249, USA.
Sci Rep. 2025 Jan 30;15(1):3740. doi: 10.1038/s41598-025-88390-w.
The inherently stochastic nature of radiation emissions makes modeling background radiation structure a particularly challenging research area. In source identification scenarios, which are critical to nuclear security, the complexity of background radiation modeling is intensified by dynamically changing factors that influence radiation measurements. Consequently, accurately modeling and estimating background radiation can significantly improve our nuclear security capabilities by enhancing the detection of anomalies within radiation data. This study introduces a new data-driven approach to modeling background radiation from spectral measurements. By leveraging the novel data mining technique, Matrix Profile (MP), this approach identifies structural patterns within radiation measurements. The method was tested on real-world background 1-second spectral data collected across various locations, with results demonstrating MP's effectiveness in modeling background structures for measurements taken in the same location. Additionally, MP modeling outperformed the traditional method of using raw measurement averages, particularly in generating distinct models for low-count backgrounds from different locations.
辐射发射固有的随机性使得对背景辐射结构进行建模成为一个特别具有挑战性的研究领域。在对核安全至关重要的源识别场景中，影响辐射测量的动态变化因素加剧了背景辐射建模的复杂性。因此，通过增强对辐射数据中异常情况的检测，准确地对背景辐射进行建模和估计可以显著提高我们的核安全能力。本研究引入了一种新的数据驱动方法，用于根据光谱测量对背景辐射进行建模。通过利用新颖的数据挖掘技术——矩阵轮廓（MP），该方法识别辐射测量中的结构模式。该方法在从不同地点收集的真实世界背景1秒光谱数据上进行了测试，结果表明MP在对同一地点的测量进行背景结构建模方面是有效的。此外，MP建模优于使用原始测量平均值的传统方法，特别是在为来自不同地点的低计数背景生成不同模型方面。
