Wael Farah, a PhD candidate at the Australia-based Swinburne University of Technology (Swinburne), has earned the title as the first individual to discover FRB through the use of a fully-automated, machine learning system.
FRBs are defined by Swinburne as “bursts of radio emission that have durations of milliseconds and exhibit the characteristic dispersion sweep of radio pulsars.”
Farah, along with a team of researchers at the Molonglo Radio Observatory near the Australian capital of Canberra, were able to use the system’s quick detection capabilities to view and assess fine details about FRBs to a degree that was previously impossible.
Since Farah’s development of the system, a total of five bursts were detected between June 2017 and December 2018. His findings have since been published in the Monthly Notices of the Royal Astronomical Society.
Farah and Swinburne’s findings are listed as a joint collaborative effort with the University of Sydney, the owner of the Molonglo telescope.
“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,” Farah said in a press release published Monday by Swinburne.
The student notes that from studying FRBs, researchers can learn more about matter that lies around and in between galaxies.
“[Farah] 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,” Dr. Chris Flynn, a Molonglo project scientist affiliated with Swinburne.
Of Farah’s findings, one is noted as the broadest and most energetic bursts to date. The student’s efforts come on the heels of Dr. Ryan Shannon’s 2018 discovery of 20 FRBs, almost doubling the total known bursts documented at the time.