AI system helps track mysterious cosmic signals
An artificial intelligence system has helped discover 72 new fast radio bursts from a mysterious source about three billion light years away from Earth, scientists say
An artificial intelligence system has helped discover 72 new fast radio bursts from a mysterious source about three billion light years away from Earth, scientists say.
Fast radio bursts are bright pulses of radio emission mere milliseconds in duration, thought to originate from distant galaxies. The source of these emissions is still unclear, however. Theories range from highly magnetised neutron stars blasted by gas streams from a nearby supermassive black hole, to suggestions that the burst properties are consistent with signatures of technology developed by an advanced civilisation.
"This work is exciting not just because it helps us understand the dynamic behaviour of fast radio bursts in more detail, but also because of the promise it shows for using machine learning to detect signals missed by classical algorithms," said Andrew Siemion, principal investigator for Breakthrough Listen, an initiative led by the University of California, Berkeley in the US to find signs of intelligent life in the universe.
Breakthrough Listen is also applying the successful machine-learning algorithm to find new kinds of signals that could be coming from extraterrestrial civilisations. While most fast radio bursts are one-offs, the source here, FRB 121102, is unique in emitting repeated bursts.
This behaviour has drawn the attention of many astronomers hoping to pin down the cause and the extreme physics involved in fast radio bursts. The artificial intelligence (AI) algorithms dredged up the radio signals from data were recorded over a five-hour period on August 26, 2017, by the Green Bank Telescope in West Virginia.
An earlier analysis of the 400 terabytes of data employed standard computer algorithms to identify 21 bursts during that period. All were seen within one hour, suggesting that the source alternates between periods of quiescence and frenzied activity, said postdoctoral researcher Vishal Gajjar, fron UC Berkeley. Researchers subsequently developed a new, powerful machine-learning algorithm and reanalysed the 2017 data, finding an additional 72 bursts not detected originally.
This brings the total number of detected bursts from FRB 121102 to around 300 since it was discovered in 2012. "This work is only the beginning of using these powerful methods to find radio transients," said Zhang.