Wael Farah, a fourth year PhD student, developed the FRB detection system and is the first person to discover FRBs in real-time with a fully automated, machine learning system.

The Melbourne-based university says Wael Farah’s system has already identified five bursts – including one of the most energetic ever detected, as well as the broadest.

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 (a thousandth of a second) and their cause is one of astronomy’s biggest puzzles.

He trained the on-site computer at the Molonglo Radio Observatory near Canberra to recognise the signs and signatures of FRBs, and trigger an immediate capture of the finest details seen to date.

Farah says his interest in FRBs comes from the fact they can potentially be used to study matter around and between galaxies that is otherwise almost impossible to see.

“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,” he says.

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.”

Australian Research Council Laureate Fellow and project leader, Professor Matthew Bailes says: “Molonglo’s real-time detection system allows us to fully exploit its high time and frequency resolution and probe FRB properties that were previously unobtainable.”

The FRBs were found as part of the UTMOST FRB search program - a joint collaboration between Swinburne and the University of Sydney, which owns the Molonglo telescope.