A Swinburne PhD student has built an automated system featuring artificial intelligence (AI) to detect and capture the details of fast radio bursts in real time.
Fast radio bursts (FRBs) are mysterious and powerful flashes of radio waves from space, thought to originate billions of light years from the Earth. They last for only a few milliseconds and represent one of astronomy’s biggest puzzles as they release in a few milliseconds more energy than the Sun does in a month!
Wael Farah developed the real-time FRB detection system to become the first person to discover FRBs with a fully automated machine-learning system. Instead of waiting months or years, FRBs are now found in seconds!
The results have been published in the Monthly Notices of the Royal Astronomical Society.
Detecting bursts in real time
The bursts were detected within seconds of their arrival at the Molonglo Radio Telescope, producing the highest quality data that allowed Swinburne researchers to study their structure accurately.
Prior to the development of the machine-learning system, astronomers had to search through data by eye – a time-consuming and less accurate process.
“An automated detection system for FRBs running on a telescope with a wide field of view to catch as many FRBs as possible is what was needed. This is what the Molonglo Radio Telescope in Australia offers,” says Mr Farah.
“It is fascinating to discover that a signal that travelled halfway through the universe, reaching our telescope after a journey of a few billion years, exhibits complex structure, like peaks separated by less than a millisecond.”
Mr. Farah trained the on-site computer at the Molonglo Radio Observatory to recognise tell-tale signs and signatures of FRBs, and trigger an immediate capture of the finest details seen to date.
Molonglo Project Scientist, Dr Chris Flynn says: “Wael has used machine-learning on our high -performance computing cluster to detect and save FRBs from amongst millions of other radio events, such as mobile phones, lightning storms, and signals from the Sun and from pulsars.”
Finding FRBs within seconds of their arrival at the telescope meant that the team could immediately scrutinise the data and discover that some actually comprised of distinct humps – a clue to their origin and ruling out the models that do not predict multiple components.
Australian Research Council Laureate Fellow and project leader, Professor Matthew Bailles says: “Molonglo’s real-time detection system allows us to fully exploit its high time and frequency resolution and probe FRB properties previously unobtainable. It is revealing hidden secrets.”
Image caption: One of the Fast Radio Bursts discovered shows remarkable structure in time and in radio frequency. The fine details seen here could only be captured because the computers had been trained to spot FRBs within seconds of their arrival at the Earth. The fine details of individual bursts can help astronomers understand the bursts themselves and also the imprints left on the radio signal as it travels for billions of years through intergalactic space. Image credit: Wael Farah/Swinburne University of Technology
Each burst tells a different story
Dr Flynn says each of the five bursts that Mr Farah’s system has identified tells a different story and show a range of unknown properties.
One of the bursts is one of the most energetic FRBs ever detected, probing into the high luminosities these events can attain.
Another is the broadest, lasting for approximately 30 milliseconds, while another is the shortest ever, detected at approximately 400 microseconds.
The Molonglo telescope is currently being upgraded to allow the precise point on the sky from which the FRBs are seen – revealing the distant host galaxies from which they originate.
The FRBs were found as part of the UTMOST FRB search program – a joint collaboration between Swinburne and the University of Sydney. The Molonglo telescope is owned by the University of Sydney.
Mr Wael Farah
Swinburne University of Technology
Dr. Chris Flynn
Swinburne University of Technology
Katherine Moody (Advisor)
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