Detection of the Illicit Movement of Nuclear Materials with Big Data
Addressing the Problem
Detecting terrorist movement of nuclear or radiological materials is of paramount importance to society. Standoff detection of nuclear or radiological materials, shielded or otherwise, is challenging because the signal at large distances is small, the background and noise are large, and signal acquisition times need to be short (thus further hampering the statistics of detection). Current practice involves searching for radioactive sources either through passive detection of the characteristic gamma rays and/or neutrons emitted from the material or through an active approach where a target or region is interrogated with probing radiation. Both of these approaches are limited by a fundamental problem: the amplitude of the radiation signal of interest decreases as the square of the distance to the source, known as the “1/r2 problem.” It is not possible to solve the 1/r2 problem. Therefore, many security-based organizations have extremely limited options in their detector selection because having detectors with high efficiency is the only way to decrease the impact of having fewer incident quanta of radiation at a large distance when only a single detector is used.
A first-of-its-kind collaboration between radiological engineers, computer scientists, and GIS experts will explore how the fusing of publicly available data with a radiation sensor network to locate radiation sources, either fixed or moving. The methodologies developed in this project will likely be applicable not only to the detection of the illicit movement of nuclear weapons, but also to any other terrorist threat device such as chemical, biological, or explosive weapons.