Based on initial results, there is a slight chance the new method may have unearthed non-Earth-based “technosignatures”. That would mean it had achieved SETI’s goal of finding signs of extraterrestrial intelligence.
In a new paper published in the journal Nature Astronomy, Ma describes how he trained a machine-learning algorithm on 480 hours of telescope data from 820 stars collected in 2016. The algorithm identified eight signals of interest that previous algorithms had failed to detect.
Ma, an undergraduate at the University of Toronto, told VICE in an interview that their method completely removes humans from the equation, unlike previous machine learning algorithms applied to SETI data.
“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,” Ma explained to VICE.
The result of Ma and colleagues’ experiment is that we now have eight signals that may have originated from advanced extraterrestrial species. Ma’s algorithm specifically pinpointed signs that “are narrow band, doppler drifting signals originating from some extraterrestrial source.”
Ma chose to use a machine learning neural network because this allows adaptability that is not afforded by more traditional artificial intelligence algorithms. “The issue is that the nature of an ET signal is not completely known,” he told VICE, “hence our proposed approach is to just learn it.”
Though Ma and colleagues’ algorithm pinpointed eight unique signals, there is no guarantee that these signals did, indeed, originate from alien civilizations.
The next step is for researchers to investigate the signals in more detail and determine whether it’s worth carrying out follow-up observations on the regions of space from which the mystery signals originated.