Source injection distribution functions for alarm algorithm testing
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Developing and testing improved alarm algorithms is a priority of the Radiation Portal Monitor Project (RPMP) at PNNL. Improved algorithms may reduce the potential impediments that radiation screening presents to the flow of commerce, without affecting the detection sensitivity to sources of interest. However, assessing alarm-algorithm performance involves calculation of both detection probabilities and false alarm rates. For statistical confidence, this requires a large amount of data from drive-through (or “dynamic”) scenarios both with, and without, sources of interest, but this is usually not feasible. Instead, an “injection-study” procedure is used to approximate the profiles of drive-through commercial data with sources of interest present. This procedure adds net-counts from a pre-defined set of simulated sources to raw, gross-count drive-through data randomly selected from archived RPM data. The accuracy of the procedure — particularly the spatial distribution of the injected counts — has not been fully examined. This report describes the use of previously constructed and validated MCNP computer models for assessing the current injection-study procedure. In particular, this report focuses on the functions used to distribute the injected counts throughout the raw drive-through spatial profiles, and for suggesting a new class of injection spatial distributions that more closely resemble actual cargo scenarios.
KeywordsFalse Alarm Rate Pacific Northwest National Laboratory Background Count Rate Simulated Source Naturally Occur Radioactive Material
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