Ph.D. Student Creates Revolutionary FRB-Detecting System

A Ph. D student from Swinburne successfully created a revolutionary artificial intelligence automated system that can detect and capture fast radio bursts (FRBs).

Fast radio bursts consist of strangely powerful radio waves originating from outer space that represent a mystery to scientists. Most experts believe these waves travel across the universe from a distance of billions of light-years before they reach our home planet. The flashes are only a few milliseconds long, and their source is still unknown.

The creator of the AI FRB detection system, Wael Farah, managed to achieve a goal many scientists attempted. He discovered FRBs in real-time using his fully automated machine learning system. Farah detected a total of five bursts, one of which could be classified among the broadest and most powerful ones ever detected.

Farah’s extraordinary achievement was documented in an article published in the Monthly Notices of the Royal Astronomical Society.

Many of you may wonder how did the Ph.D. student manage to develop such a system. It seems that Farah trained the computer at the Molonglo Radio Observatory to recognize the characteristics of FRBs. When the system detects them, it instantaneously captures the details of the flash.

The already-discovered bursts were detected within seconds, collecting precious data that researchers at Swinburne were able to analyze meticulously and gather more knowledge about the origin of the strange radio waves.

One of the captured FRBs showed a special structure in terms of time and frequency.

According to Farah, fast radio bursts can be used to study invisible matter in the universe. He said: “It is fascinating to discover that a signal that traveled 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.”

Dr. Chris Flynn, a Molonglo project scientist, offers another explanation of Farah’s discovery. According to him, the Ph.D. Student used machine learning to differentiate FRBs from millions of other radio events, such as the signals emitted by mobile phones, lightning storm, and other radio waves emitted from within our galaxy.

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