Studies that were previously working on fast radio bursts concluded that 121102 emits a signal from a galaxy located 3 billion light years away from us, but that's as far as the study goes.
Fast radio bursts are among the most mysterious occurrences in the Universe, and a few have also speculated that they may originate from alien technology. 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).
The UC Berkeley "Breakthrough Listen" program used machine learning to identify 72 new recordings known as fast radio bursts that came from a odd repeating burst known as FRB 121102.
Knowing that human observations aren't always ideal, UC Berkeley Ph.D. student Gerry Zhang created a machine learning algorithm to apply to the dataset, hoping to pick up any FRBs that the researchers might have missed. One FRB source has attracted attention because it repeats. Twenty-one of those bursts came during a one-hour period on August 26, 2017, which was part of a larger five-hour recording session that produced 400 terabytes of data. 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.
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. This brings the total number of detected bursts from FRB 121102 to around 300 since its discovery. 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.
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"This work is only the beginning of using these powerful methods to find radio transients", said Gerry Zhang. "We hope our success may inspire other serious endeavors in applying machine learning to radio astronomy". Just as the patterns of pulses from pulsars have helped astronomers constrain computer models of the extreme physical conditions in such objects, the new measurements of FRBs will help figure out what powers these enigmatic sources, Siemion said. According to a University of California, Berkeley press release, the researchers "trained an algorithm known as a convolutional neural network" to replicate traditional methods of detecting the bursts.
"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. They used the Breakthrough Listen digital instrumentation at the GBT.
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.
For a decade, astronomers relish puzzled over ephemeral however extremely noteworthy radio bursts from home.