Australian-built AI automates fast radio burst detection

By on 6 August, 2019

Wael Farah, Australian PhD candidate at Swinburne University, has become the first person in the world to detect a fast radio burst (FRB) in real time with an automated machine learning system.

FRBs are extremely powerful, brief flashes of radio energy lasting up to several milliseconds that may be instrumental in a better understanding of the universe’s key metrics.

The system has already identified five FRBs, after his training and deployment of his own machine learning-based detection system, deployed at Molonglo Radio Observatory, near Canberra.

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 said.

Molonglo project scientist, Dr. Chris Flynn praised the results of Farah’s system. “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,” he said.

The FRBs were found as part of the UTMOST FRB search program – a joint collaboration between Swinburne and the University of Sydney.

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