The SETI Institute Applies Artificial Intelligence Technology to Detect Fast Radio Bursts; A Marker of Extra-Terrestrial Life
05 October 2018

The SETI Institute Applies Artificial Intelligence Technology to Detect Fast Radio Bursts; A Marker of Extra-Terrestrial Life

The SETI Institute has been using IBM Cloud and AI algorithms to analyze over 20 million signals captured by the ATA radio telescopes.

Machine learning techniques are now being used to directly detect fast radio bust (FRB) that may be one of the signatures of intelligent life in the universe. Applying machine learning for signal detection promises to open up new avenues for identifying signals from extraterrestrial intelligence.

Researchers at Breakthrough Listen – the initiative to find signs of intelligent life in the universe – have applied machine learning techniques to detect 72 new fast radio bursts emanating from the "repeater" FRB 121102. Fast radio bursts, or FRBs, are bright pulses of radio emission, just milliseconds in duration, thought to originate from distant galaxies.

Most FRBs have been witnessed during just a single outburst. In contrast, FRB 121102 is the only one to date known to emit repeated bursts, including 21 seen during Breakthrough Listen observations made in 2017 with the Green Bank Telescope (GBT) in West Virginia.

FRBs from 121102 originate in a dwarf galaxy about 3 billion light years from Earth. The nature of the object emitting them is unknown. There are many theories, including that they could be the signatures of technology developed by extraterrestrial intelligent life.

In August of 2017, the Listen science team at the University of California, Berkeley SETI Research Center observed FRB 121102 for five hours, using digital instrumentation at the GBT.

Combing through 400 TB of data, they reported a total of 21 bursts. All were seen within one hour, suggesting that the source alternates between periods of quiet and intense activity.

New machine learning algorithms have been developed and will be applied to re-analyze the 2017 data. The algorithm known as a convolutional neural network can recognize bursts and will be set loose on the 400 TB dataset to find bursts that may have been missed using older approaches to detection.

The SETI Institute has been using IBM Cloud and AI algorithms to analyze over 20 million signals captured by the ATA radio telescopes, using the power of machine learning to greatly improve how anomalous signals of interest can be identified and flagged for further examination.  Scientists at the SETI Institute have used use the IBM Cloud to search for signal.

New FRB research may provide clues about whether or not there are signatures of extraterrestrial technology in the vast troves of collected data.

http://seti.berkeley.edu/frb-machine

 

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