Researchers at the University of Michigan in Ann Arbor have developed a new algorithm that could enable faster, less expensive detection of weapons-grade nuclear materials at borders, quickly differentiating between benign and illicit radiation signatures in the same cargo.
For the project, they collaborated with researchers at the University of Illinois, Los Alamos National Laboratory in New Mexico, and Heriot-Watt University and University of Edinburgh, both in Scotland.
“We hope that the findings will be helpful in reducing the false positive alarms at radiation portal monitors, even in scenarios with multiple sources present, and enable the use of cost-effective detectors, such as organic scintillators,” says Angela DiFulvio, assistant professor of nuclear, plasma, and radiological engineering at the University of Illinois and corresponding author of the study, which was recently published in Nature Scientific Reports.
DiFulvio is also a former postdoctoral researcher in the Detection for Nuclear Nonproliferation Group at U-M, led by Sara Pozzi, professor of nuclear engineering and radiological science.
As nations work to protect their citizens from the threat of nuclear terrorism, security protocols deter and detect the smuggling of special nuclear materials — highly enriched uranium, weapons-grade plutonium, or materials that produce a lot of radiation — across any border.
The researchers developed an algorithm capable of identifying weak radiation signals, such as what might be seen from plutonium encased materials that absorb radiation. It works even in the presence of a high radiation background, including everyday sources such as cosmic rays from space and radon from the rock underfoot.
Based on the results, the researchers believe the use of their algorithm could improve the ability of radiation portal monitors at national borders to tell the difference between potential smuggling activity and benign radiation sources. Naturally occurring radioactive materials such as ceramics and fertilizers, or radionuclides in recently treated nuclear medicine patients, can set off nuisance alarms at radiation scanning facilities.
“There’s also the concern that somebody might want to mask a radioactive source, or special nuclear material, by using naturally occurring radioactive materials such as granite or kitty litter,” says Pozzi, who is also senior author on the paper. “As vehicles or boxes are scanned, the data from the detector can be put through these algorithms that unmix the different sources. The algorithms can quickly identify whether special nuclear materials are present.”
Unmixing sources so that smuggling activity can’t be hidden among benign radiation sources is hard to do quickly. For that, the team turned to specialists in machine learning, who could use data collected by Pozzi’s group to “train” algorithms to look for the signatures of materials that could be used to make a nuclear bomb.
“We crafted an unmixing model that both reflects the basic physics of the problem and was also amenable to fast computation,” says Alfred Hero, co-author and the John H. Holland Distinguished University Professor of electrical engineering and computer science and R. Jamison and Betty Williams professor of engineering at U-M.
The research began at U-M as part of the Consortium for Verification Technology, a five-year $25 million nuclear nonproliferation research program funded by the U.S. Nuclear National Security Administration, led by Pozzi. DiFulvio continued the work when she moved to Illinois in 2018.
“This work is a powerful example of the benefit of close and sustained collaboration between computational data scientists and nuclear engineers, resulting in major improvement of nuclear radiation detection and identification,” Hero says.
Additional funding came from the U.S. National Science Foundation, U.K. Royal Academy of Engineering, Engineering and Physical Sciences Research Council, and Defence Science and Technology Laboratory.
The full study is available here.