Published: Thu, September 13, 2018
Science | By Michele Flores

Artificial Intelligence Helps Breakthrough Listen Find New Fast Radio Bursts

Artificial Intelligence Helps Breakthrough Listen Find New Fast Radio Bursts

Astronomers used artificial intelligence to spot fast radio bursts and discovered an intriguing repeating event not classified yet by scientists. The Listen science team at the University of California, Berkeley SETI Research Center, originally observed FRB 121102 on 26 August a year ago, using the Breakthrough Listen digital instrumentation.

Lickety-split radio bursts are shimmering pulses of radio emission mere milliseconds in duration, conception to originate from distant galaxies. Theories range from highly magnetized neutron stars, blasted by gas streams near to a supermassive black hole, to suggestions that the burst properties are consistent with signatures of technology developed by an advanced civilization. According to Phys.org, Breakthrough Listen, one of several Breakthrough-themed science research initiatives in cooperation with the SETI (Search for Extraterrestrial Intelligence) Institute, has successfully used artificial intelligence to increase the identification rate for recurring fast radio bursts from a deep space source known as FRB121102.

When UC Berkeley researchers and their collaborators developed a new, powerful machine-learning algorithm and reanalyzed the 2017 data, they found 72 additional bursts. In contrast, FRB 121102 is the only one to date known to emit repeated bursts, including 21 seen during Breakthrough Listen observations made in 2017 with the Green Bank Telescope (GBT) in West Virginia. They used a standard convolutional neural network for their AI, the same kind that internet search providers use to process images, and trained it to recognize the 21 bursts previously identified in the August 26 data.

This brings the total number of detected bursts from FRB 121102 to around 300 since it was discovered in 2012, researchers said.

"This work is only the beginning of using these powerful methods to find radio transients", Zhang said.

Zhang's crew passe a few of the similar tactics that net technology companies use to optimize search results and classify photography. "We hope our success would perhaps per chance impartial inspire other serious endeavors in making use of machine studying to radio astronomy".

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Researchers have since detected many more FRBs, but their origins remain a mystery to this day. The results from the AI give an insight of the periodicity of the pulses that came from 121102 and suggest it's not always the same patterns that determine when the outbursts happen.

Gerry Zhang, a Ph.D. student at the University of California, Berkeley, and co-author of the study concludes that the project is essential in understanding the Universe, even if these FRBs turn out not to be "signatures of extraterrestrial technology".

The results of this research have been accepted for publication in the Astrophysical Journal and will be available on the arXiv service on Monday 10 September, 2018. They used the Breakthrough Listen digital instrumentation at the GBT.

"This work is exciting not just because it helps us understand the dynamic behavior of fast radio bursts in more detail, but also because of the promise it shows for using machine learning to detect signals missed by classical algorithms".

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

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