ARI-MA: Machine Learning for Effective Nuclear Search and Broad-Area Monitoring

该奖项的研究目标是开发机器学习技术，以提高移动辐射探测系统的性能。这项研究将产生检测和解释这些系统收集的数据中的复杂模式的方法，同时考虑到比人类分析师能够处理的更多的信息来源和因素。开发的算法还将具有适应不断变化的条件和新出现的威胁模式的能力。这些方法将在真实世界的场景中进行测试，针对在环境中收集的数据进行测试，这些环境中充斥着不同的背景辐射，包括不构成安全威胁的天然和人造放射源。可交付成果包括已开发算法的描述、经验评估结果的文档、软件实现、出版物和教程。这项研究的结果将补充探测器技术的最新发展，以新的方式来理解数据，从而在核探测实践中产生飞跃。由此产生的算法将使系统能够由监督分类组成，以确定每个检测到的辐射源的性质，异常检测以识别新的或未知的辐射源，识别自相似异常组以发现新类型的辐射源，主动学习以指导新类型的发现和潜在威胁检测的后续工作。它们将共同提高核探测系统的灵敏度和特异性，同时控制误检率。结果，包括对来自实地测量的高质量数据的评估和在现实测试环境中进行的威胁模拟，将被传播，以便能够创建生产级系统。大部分资金用于支持研究生，他们将进行大部分研究，并将在2012-2014年完成学位，成为机器学习支持的核威胁检测多学科领域的新领导者。

The research objective of this award is to develop machine learning techniques to enhance the performance of mobile radiation detection systems. The research will produce methods for detection and explanation of complex patterns in data collected by such systems, while taking into account many more sources of information and factors than a human analyst can process. The developed algorithms will also have the ability to adapt to changing conditions and emerging patterns of threats. The methods will be tested in real-world scenarios against data collected in environments that are awash in ambient, varying background radiation, and which include natural and manufactured radioactive sources that pose no security threat. Deliverables include descriptions of developed algorithms, documentation of the results of their empirical evaluation, software implementations, publications and tutorials.The results of this research will complement recent developments in detector technology with new ways to understand the data to produce a leap forward in the practice of nuclear detection. The resulting algorithms will enable systems consisting of supervised classification to determine the nature of each detected radiation source, anomaly detection to identify new or unknown sources, identification of groups of self-similar anomalies to discover new classes of sources, and active learning to guide the discovery of new classes and follow-ups on potential threat detections. Jointly, they will allow for increased sensitivity and specificity of nuclear detection system, while containing false detection rates. The results, including evaluations against high-quality data from field measurements and threat simulations conducted in realistic test environments, will be disseminated to enable creation of production-grade systems. The majority of the funding is to support graduate students who will perform the bulk of the research, and who will finish their degrees in 2012-2014 to become new leaders in the multidisciplinary area of machine-learning-supported nuclear threat detection.