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Deep Learning Could Help Detect Nuclear Events Worldwide

Deep Learning Could Help Detect Nuclear Events Worldwide

PNNL scientists Emily Mace and Jesse Ward teamed to explore the promise of deep learning to help interpret signals from radioactive decay events, which could indicate underground nuclear testing. Mace presented their work at the 11th MARC conference — Methods and Applications of Radioanalytical Chemistry — in April 2018 in Hawaii. Deep learning enables machines to learn and make decisions without being explicitly programmed for all conditions.

To verify nuclear treaties, scientists analyze gas samples for specific types of radioactivity. Changes in levels of argon-37, for example, could indicate prior nuclear test activity, which could represent treaty violations. Mace is an expert in interpreting the features of such radioactive decay signals — their energy, timing, peaks, slopes, duration, and other features.

The challenge is that the signal hides among the "noise" of all other signals created on the planet, natural and otherwise. Mace wondered if deep learning could help. She sent Ward information on nearly 2 million energy pulses detected in PNNL"s Shallow Underground Laboratory since it opened in 2010. Ward trained the network, inputting many features of each pulse and showing the network how the data was interpreted.

The research results were impressive. Tests showed that the deep learning techniques separated signal events from instrument "noise" with nearly 100 percent accuracy, and performed 25 times better than current computational method when sorting the very toughest cases.

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July 2018

Deep Learning