Published: Thu, September 13, 2018
Science | By Joan Schultz

Artificial Intelligence Detects 72 Mysterious Radio Bursts from Space


Los Angeles, Sep 11 Scientists say they have used artificial intelligence (AI) to discover 72 new fast radio bursts from a mysterious source about three billion light years away from Earth.

To understand the new fast radio bursts better, the Breakthrough Listen team at the SETI Research Center at University of California, Berkeley observed FRB 121102 on August 26, 2017.

Fast radio bursts are fast, enormously energetic pulses originating from galaxies far away that are now poorly understood by scientists. No one knows for sure what causes these radio emissions but theories abound - from highly magnetized neutron stars battling black holes to signs of alien life. Researchers recorded radio data from this source and scraped it with a new machine learning algorithm that uncovered 72 new bursts undetected by normal techniques. In contrast, FRB 121102 is the only one to date known to emit repeated bursts, including 21 detected during Breakthrough Listenobservations made in 2017 with the Green Bank Telescope (GBT) in West Virginia. By analyzing the data using standard computer algorithms, they were able to identify 21 FRB's during the 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. This brings the total series of detected bursts from FRB 121102 to spherical 300 since it became found in 2012.

Zhang's team used some of the same techniques that internet technology companies use to optimise search results and classify images. They trained an algorithm known as a convolutional neural network to identify the earlier found FRB's, before setting it loose on the 2017 dataset.

"This work is simply the starting of the use of these noteworthy how to get radio transients", acknowledged Zhang. 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. "We hope our success may inspire other serious endeavours in applying machine learning to radio astronomy".

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". 2018) accepted for publication in the Astrophysical Journal.

Step forward Listen - the initiative to get indicators of sparkling existence in the universe - announced today that a gape of thousands and thousands of stars located in the airplane of our galaxy, the use of the CSIRO Parkes Radio Telescope ("Parkes") ...

"Gerry's work is exciting not just because it helps us understand the dynamic behavior of FRBs in more detail, but also because of the promise it shows for using machine learning to detect signals missed by classical algorithms", remarked Dr. Andrew Siemion, Berkeley SETI research center director and principal investigator for Breakthrough Listen. The data indicated that the radio waves switched between puzzling periods of extremely activity, to periods of nothing at all.

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