One of the great scientific projects of our time is the Search for Extraterrestrial Intelligence or SETI — the hunt for evidence of technologically advanced civilizations elsewhere in the galaxy. The current manifestation of this endeavor is the Breakthrough Listen Initiative, which uses radio telescopes to look for signals that cannot have a terrestrial origin.
The big challenge in this effort is combing through massive data sets — hundreds of hours of data at multiple frequencies. Researchers have found tens of millions of false positives in these datasets and so would dearly love a quicker way to filter out the weeds.
Now Peter Xiangyuan Ma at the University of Toronto in Canada, and colleagues, have trained a machine learning algorithm to do the job instead. And in putting the machine through its paces, the team say it helped identify 8 signals of interest that deserve further follow up.
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