AI can detect signs of nuclear weapons testing banned under the Comprehensive Nuclear-Test-Ban Treaty, according to research from the US Pacific Northwest National Laboratory.
A group of scientists have built a neural network to sniff out any unusual nuclear activity. Researchers from the Pacific Northwest National Laboratory (PNNL), one of the United States Department of Energy national laboratories, decided to see if they could use deep learning to sort through the different nuclear decay events to identify any suspicious behavior.
The lab, buried beneath 81 feet of concrete, rock and earth, is blocked out from energy from cosmic rays, electronics and other sources. It means that the data collected is less noisy, making it easier to pinpoint unusual activity.
The system looks for electrons emitted and scattered from radioactive particles decaying, and monitor the abundance of argon-37, a radioactive isotope of argon-39 that is created synthetically through nuclear explosions.
Argon-37 which has a half-life of 35 days, is emitted when calcium captures excess neutrons and decays by emitting an alpha particle. Emily Mace, a scientist at PNNL, said she looks for the energy, timing, duration and other features of the decay events to see if it's from nuclear testing.
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"Some pulse shapes are difficult to interpret," said Mace. "It can be challenging to differentiate between good and bad data."
Deep learning makes that process easier. Computer scientists collected 32,000 pulses and annotated their properties, teaching the system to spot any odd features that might classify a signal as ‘good’ or ‘bad’.
"Signals can be well behaved or they can be poorly behaved," said Jesse Ward. "For the network to learn about the good signals, it needs a decent amount of bad signals for comparison." When the researchers tested their system with 50,000 pulses and asked human experts to differentiate signals, the neural network agreed with them 100 per cent of the time.
It also correctly identified 99.9 per cent of the pulses compared to 96.1 per cent from more conventional techniques.
"Deep learning is making it easier for us to filter out a small number of good events that are indicative of the activity of interest," said Craig Aalseth, a nuclear physicist and PNNL laboratory fellow.
The results were presented at the Methods and Applications of Radioanalytical Chemistry conference in Hawaii this year. ®