Using telescope data that was first collected in 2016, the machine-learning algorithm analyzedver 480 hours of data from 820 stars and identified eight signals of interest that previous algorithms had not detected.
Peter Ma is first author on the paper and an undergraduate student at the University of Toronto. He told Motherboard in an email that while AI has been applied to SETI’s radio data in the past, this new approach takes the search completely out of human hands.
“Previously people have inserted ML [machine learning] components into various pipelines to help with the search,” Ma said. “This work relies entirely on just the neural network without any traditional algorithms supporting it and produced results that traditional algorithms did not pick up.”
Radio data streaming in from outer space is an abundant resource, but it’s also one that can be easily muddled with our own Earth-based radio signals. Ma said that signals of interest that are plucked from this haze are ones that “are narrow band, doppler drifting signals originating from some extraterrestrial source.”
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