Breakthrough Listen uses machine learning to find fast radio bursts

Step forward Listen researchers passe synthetic intelligence to search via radio signals recorded from a quickly radio burst, taking pictures many extra than humans would perhaps.

Synthetic intelligence is invading many fields, most not too long in the past astronomy and the be taught about sparkling existence in the universe, or SETI. Previous studies have shown that the bursts from 121102 are emanating from a galaxy 3 billion light years from Earth, but the nature of the object emitting them is still unknown. The availability of these emissions is mute unclear, on the opposite hand. Some theories, however, justify that their properties are consistent with signatures of technology developed by an advanced civilization.

Scientists who participate in project Breakthrough Listen, using the method of machine learning analyzed the signals from an unknown distant source.

This behaviour has drawn the attention of many astronomers hoping to pin down the cause and the extreme physics involved in fast radio bursts.

The data comes from the Green Bank Telescope in West Virginia (above), which was pointed toward this source of fast and bright (hence the name) bursts for five hours in August of 2017.

Combing through 400 terabytes of data, they reported a total of 21 bursts. The 21 fast radio bursts were all seen within one hour, which suggests that whatever the source of FRB 121102 is, it demonstrated a period of excessive activity.

Now, UC Berkeley PhD student Gerry Zhang and collaborators have developed a new, powerful machine learning algorithm, and reanalyzed the 2017 GBT dataset, finding an additional 72 bursts that were not detected originally.

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"This work is only the beginning of using these powerful methods to find radio transients", said Gerry Zhang. All Zhang and his team had to do were to optimize the algorithm like internet tech companies do when it comes to optimizing search results or image classification. The algorithm known as the convolutional neural network was able to recognize those radio bursts that other systems tend to miss. 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. "It has advanced our knowledge of one of the most tantalizing mysteries in astronomy", said Pete Worden of the Breakthrough Initiatives.

"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", he added.

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.

Fast radio bursts are enigmatic radio signals from space that are hard to detect and source. They used the Breakthrough Listen digital instrumentation at the GBT.

It is called the Breakthrough Listen program, and it uses AI technology to search for fast radio bursts (FRBs).

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