Sunday, 26 May, 2019

Artificial Intelligence Aiding the Search for Aliens

Green Bank is part of the US Radio Quiet Zone where wireless telecommunications signals are banned to prevent transmissions interfering with a number of radio telescopes Image The mysterious signals were found in data collected by the Green Bank Telescope in the US
Sandy Nunez | 13 September, 2018, 21:31

They managed to detect those weird FRBs using the newly developed artificial intelligence that reviewed the available data on these mysterious events and identified several detections that had been previously unseen.

"These results hint that there could be vast numbers of additional signals that our current algorithms are missing and clearly demonstrate the power of applying modern data analytics and AI tools to astronomical research", said SETI Institute President and CEO Bill Diamond.

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. The source of these emissions is still unclear, however. Theories explaining their origin include that they are caused by polarised waves travelling through strong magnetic fields in dense plasma (such as from a neutron star in the cosmic neighbourhood of a galactic core's supermassive black hole or within dense, magnetised nebulas).

For the first time a fast radio bursts (Fast Radio Bursts, FRB) was seen in 2007, however the recognized explanation, they have not received so far.

"This work is exciting not just because it helps us understand the dynamic behavior of fast radio bursts in more detail, he said". By analyzing the data using standard computer algorithms, they were able to identify 21 FRB's during the period.

UC Berkeley Ph.D. student Gerry Zhang and collaborators this capability that of this truth developed a unique, noteworthy machine-studying algorithm and reanalyzed the 2017 knowledge, discovering an additional seventy two bursts not detected first and main. Thus, the discovery brought the total number of detected bursts from FRB 121102 to 300 since 2012.

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"This work is only the beginning of using these powerful methods to find radio transients", Zhang said. "We hope our success would perhaps per chance impartial inspire other serious endeavors in making use of machine studying to radio astronomy".

Zhang's team used some of the same techniques that internet technology companies use to optimize search results and classify images. They trained an algorithm is named a convolutional neural community to glimpse bursts came upon by the classical search technique passe by Gajjar and collaborators, after which voice it free on the dataset to get bursts that the classical technique missed. 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.

One of astronomy's controversial mysteries is now being investigated by artificial intelligence.

Whether or not FRBs themselves eventually turn out to be signatures of extraterrestrial technology, Breakthrough Listen is helping to push the frontiers of a new and rapidly growing area of our understanding of the Universe around us.

A paper on the research was recently accepted for publication in The Astrophysical Journal.

By Geremia at English Wikipedia [Public domain], via Wikimedia CommonsBreakthrough Listen, a program that is looking for aliens throughout the universe, has detected 72 new fast radio bursts thanks to an applied machine learning algorithm.