Los Angeles: Scientists say they have used artificial intelligence (AI) to discover 72 new fast radio bursts from a mysterious source about three billion light-years away from Earth.Also Read - Flying to US? Fresh Guidelines Issued For International Travel | Read New Rules
The initiative may advance the search to find signs of intelligent life in the universe, said researchers from the University of California, Berkeley in the US. Also Read - Woman Raped on Train While Passengers Remain Mute Spectators
Fast radio bursts are bright pulses of radio emission mere milliseconds in duration, thought to originate from distant galaxies. Also Read - US President Joe Biden Plan Seeks to Expand Education, From Pre-K to College
However, the source of these emissions is still unclear, according to the research published in The Astrophysical Journal.
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 civilization.
“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 from the University of California – Berkele.
Researchers are 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 AI algorithms dredged up the radio signals from data were recorded over a five-hour period in 2017, by the Green Bank Telescope in West Virginia in the US.
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 Berkeley postdoctoral researcher Vishal Gajjar.
The researchers developed the 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, researchers said.