Detection of radioactive material entering national ports: A Bayesian approach to radiation portal data

TL;DR

This paper introduces a Bayesian classification method for real-time detection of radioactive materials at ports, aiming to enhance security by improving inspection accuracy and efficiency.

Contribution

It presents a novel Bayesian approach tailored for radiation portal data, with analysis of its computational properties and simulation-based validation.

Findings

Effective in simulations for detecting radioactive cargo

Computationally feasible for real-time implementation

Supports large-scale port inspections

Abstract

Given the potential for illicit nuclear material being used for terrorism, most ports now inspect a large number of goods entering national borders for radioactive cargo. The U.S. Department of Homeland Security is moving toward one hundred percent inspection of all containers entering the U.S. at various ports of entry for nuclear material. We propose a Bayesian classification approach for the real-time data collected by the inline Polyvinyl Toluene radiation portal monitors. We study the computational and asymptotic properties of the proposed method and demonstrate its efficacy in simulations. Given data available to the authorities, it should be feasible to implement this approach in practice.