Alien hunters say they have detected more unusual radio signals from a galaxy 3 billion light years away with the help of artificial intelligence.
Researchers at the Search for Extraterrestrial Intelligence, or SETI, used a machine-learning algorithm to sift through a trove of data collected by West Virginia’s Green Bank Telescope a year ago. The AI found 72 more “fast radio bursts” — on top of the 21 FRBs first detected on Aug. 26, 2017 — emanating from galaxy FRB 121102, bringing the total that day to 93.
“Not all discoveries come from new observations,” Pete Worden, executive director of Breakthrough Initiatives, a SETI project, said in a statement. “In this case, it was smart, original thinking applied to an existing dataset. It has advanced our knowledge of one of the most tantalizing mysteries in astronomy.”
Fast radio bursts remain one of the most head-scratching phenomena in the universe. Only about 30 events have been confirmed since they were discovered over a decade ago and each flare only lasts a few milliseconds.
FRBs are typically one-time events — which makes FRB 121102 particularly interesting as it’s given off hundreds of bursts. Including the 93 FRBs in August 2017, the galaxy has been the source of nearly 300 since it was first discovered in 2012.
Some astronomers believe they’re incredibly common but that our current technology isn’t sensitive enough to consistently detect them. Scientists have proposed a number of theories about what causes the mysterious signals, ranging from black holes and neutron stars to dark matter and alien civilizations.
Researchers say there was no pattern to the new FRBs — neither confirming nor ruling out the notion that extraterrestrial life could be trying to contact us.
The most recent AI-detected bursts give researchers more to work with as they try to pinpoint the source.
“This work is only the beginning of using these powerful methods to find radio transients,” Gerry Zhang, a doctoral student at UC Berkeley who led the AI development, said in a statement. “We hope our success may inspire other serious endeavors in applying machine learning to radio astronomy.”
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