Published: Thu, September 13, 2018
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Artificial Intelligence Helps Breakthrough Listen Find New Fast Radio Bursts

Artificial Intelligence Helps Breakthrough Listen Find New Fast Radio Bursts

In a recent study, the SETI researchers used a customized A.I. system to discover dozens of previously unidentified fast radio bursts from a source some 3 billion light-years away.

FRBs are known to be millisecond pulses of radio emission from galaxies far away.

The UC Berkeley "Breakthrough Listen" program used machine learning to identify 72 new recordings known as fast radio bursts that came from a unusual repeating burst known as FRB 121102. Most fast radio burst signals are one-time events, so the fact that FRB121102 sends them out repeatedly indicates that there is something different about this source that we don't understand yet. This behavior has drawn the attention of many astronomers hoping to pin down the cause and the extreme physics involved in fast radio bursts. An earlier analysis of the 400 terabytes of data employed standard computer algorithms to identify 21 bursts during that period. All had been seen within one hour, suggesting that the provide alternates between sessions of quiescence and frenzied process, acknowledged Berkeley SETI postdoctoral researcher Vishal Gajjar.

Next, UC Berkeley Ph.D. student Gerry Zhang and a few collaborators developed a new, powerful machine-learning algorithm - using similar techniques implemented to optimize search engine results. Thus, the discovery brought the total number of detected bursts from FRB 121102 to 300 since 2012.

Zhang's team used some of the same techniques that internet technology companies use to optimise search results and classify images.

"This work is animated not impartial since it helps us perceive the dynamic habits of swiftly radio bursts in extra detail, however moreover thanks to the promise it reveals for the use of machine studying to detect signals missed by classical algorithms", acknowledged Andrew Siemion, director of the Berkeley SETI Examine Middle and fundamental investigator for Step forward Listen, the initiative to get indicators of sparkling existence in the universe. "We hope our success may inspire other serious endeavours in applying machine learning to radio astronomy".


To make the discovery, Zhang and his team used a convolutional neural network, a type of algorithm modeled off the human brain, which has been used to find craters on the moon and help detect earthquakes.

Researchers have since detected many more FRBs, but their origins remain a mystery to this day.

According to the press release, though, the researchers did not find anything to suggest an artificial origin - they detected no pattern to the bursts, "at least if the period of that pattern is longer than about 10 milliseconds".

Breakthrough Listen is a scientific program in search for evidence of technological life in the Universe.It aims to survey one million nearby stars, the entire galactic plane and 100 nearby galaxies at a wide range of radio and optical bands. Their findings, accepted for publication in the Astrophysical Journal, give insight into the radio waves' periodicity and specific frequencies, but unfortunately leave us with more questions than answers.

For a decade, astronomers relish puzzled over ephemeral however extremely noteworthy radio bursts from home.

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