Artificial Intelligence Helps Find New Fast Radio Bursts

Искусственный интеллект обнаружил 72 сигнала инопланетян

Artificial Intelligence Helps Find New Fast Radio Bursts

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. These bursts are extremely bright flashes of radio light that travel billions of light years from beyond our galaxy to reach Earth and appear momentarily on the sky. Theories range from highly magnetized neutron stars blasted by gas streams from a nearby supermassive black hole, to suggestions that the burst properties are consistent with signatures of technology developed by an advanced civilization. Breakthrough Listen found 23 bursts from 121102 by using the Green Bank Telescope - West Virginia.

When UC Berkeley researchers and their collaborators developed a new, powerful machine-learning algorithm and reanalyzed the 2017 data, they found 72 additional bursts.

While most swiftly radio bursts are one-offs, the provide right here, FRB 121102, is distinctive in emitting repeated bursts. 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 evaluation of the four hundred terabytes of knowledge employed recurring computer algorithms to title 21 bursts towards that period.

"All were seen within one hour, suggesting that the source alternates between periods of quiescence and frenzied activity", said Berkeley postdoctoral researcher Vishal Gajjar.

"Together with the 21 previously reported pulses, this observation marks the highest number of FRB 121102 pulses from a single observation, totaling 93 pulses in five hours, including 45 pulses within the first 30 minutes".

"This work is simply the starting of the use of these noteworthy how to get radio transients", acknowledged Zhang. The technique the team used resembles algorithms used to optimize search results on search engines and to classify images. "We hope our success may inspire other serious endeavours in applying machine learning to radio astronomy".

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

In their recent study, the researchers trained their algorithm on simulated signals, teaching it to recognize signs of fast radio bursts, and then "let the trained network loose on the data containing the real signals", Zhang said.

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".

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".

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") ...

The new results are described in an article accepted for publication in The Astrophysical Journal and is available for download from the Breakthrough Listen website.

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