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Researchers Exploring Listen, a multi-million dollar campaign to look for signals from foreign civilizations, who still do not know exactly what causes repeated radio wave bursts in a distant galaxy. source, no matter what it turns out to be.
A team led by Gerry Zhang, a student at the University of California at Berkeley, has developed a new type of machine learning algorithm to analyze data collected a year ago during an observation campaign using the Green Bank Telescope in West Virginia.
The campaign is focused on a radio source known as FRB 121102, located in a dwarf galaxy 3 billion light-years away from the Auriga constellation. Astronomers have observed a lot of fast radio gusts over the last ten years, each lasting only a few milliseconds. Only FRB 121102 was found to send repeated bursts.
A number of theories have been proposed to explain the bursts, ranging from interactions involving magnetized neutron stars and black holes to deliberate signaling by advanced civilizations.
The researchers of Breakthrough Listen, one of the many space projects supported by Russian billionaire Yuri Milner, added to the mystery of last year when they organized a six-hour listening session on August 26, 2017.
Their initial analysis, using standard search algorithms, revealed that 21 gusts occurred during the first hour of surveillance. The radio source seemed to be quiet.
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To verify the data, Zhang and his team used machine learning techniques originally designed to optimize search results and classify images. They formed another type of algorithm called convolutional neural network on the examples of bursts found using more traditional methods, and then they moved the AI algorithm on the complete dataset to search for d & rsquo; Other gusts that could have been missed.
The AI algorithm found 72 more bursts, bringing to about 300 the total number of fast radio bursts that occurred at FRB 121102 since it was discovered in 2012. An article reporting the latest findings has been accepted for publication in The Astrophysical Journal .
The updated analysis indicates that there is no predictable pattern of gust recurrence, at least on time scales greater than 10 milliseconds. The new discoveries are likely to put new constraints on the various hypotheses considered as the cause of the bursts and thus contribute to the resolution of the mystery.
"This work is only the beginning of the use of these powerful methods to find radio transients," Zhang said. "We hope our success could inspire further serious efforts in the application of machine learning to radio astronomy."
Andrew Siemion, director of the Berkeley SETI Research Center and principal investigator of Breakthrough Listen, said the methods could also solve other problems in finding foreign signals.
"As the FRBs themselves turn out to be signatures of extraterrestrial technologies, Breakthrough Listen is helping to push the boundaries of a new and growing field of our understanding of the universe around us," he said.
Could artificial intelligence find an extraterrestrial intelligence? Stay tuned …
<p class = "canvas-atom-canvas-text Mb (1.0em) Mb (0) – smt Mt (0.8em) – sm" type = "text" content = "In addition to Zhang and Siemion, the authors of "Detecting and Periodic Fast Pulse Radio Burst 121102: A Machine Learning Approach" include Vishal Gajjar, Griffin Foster, James Cordes, Casey Law and Yu Wang. Check out the Berkeley SETI Research Center website for more information, including the complete manuscript before printing."data-reactid =" 38 ">In addition to Zhang and Siemion, the authors of "Detecting and Periodic Fast Pulse Radio Burst 121102: A Machine Learning Approach" include Vishal Gajjar, Griffin Foster, James Cordes, Casey Law and Yu Wang. Check out the Berkeley SETI Research Center website for more information, including the complete manuscript before printing.
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