Alien-Hunting Scientists Find 72 New Fast Radio Bursts Thanks To New Artificial Intelligence

Fast radio bursts (FRBs) are extremely powerful energetic emissions from the distant universe that last just a few milliseconds on average. The origin of most FRBs is still unclear because they are one-off events. But one of them, FRB 121102, has been repeating itself, allowing researchers to find out a little bit more about these mysterious occurrences.

Last year, astronomers used the Green Bank Telescope to discover more FRBs from this source and found 21 new bursts. But more was hiding in the data. Researchers used a new machine-learning algorithm to reanalyze the 2017 data and discovered 72 new bursts that weren’t spotted in the original analysis. The discovery has been accepted for publication in the Astrophysical Journal.

The signals are thought to be coming from a neutron star located in a dwarf galaxy roughly 3 billion light-years from Earth. The neutron star is likely in a highly magnetized environment like the remnant of a supernova or near a black hole. Thanks to the latest analysis, there have now been roughly 300 FRBs detected from this one source, a great step forward in understanding its nature.

“This work is only the beginning of using these powerful methods to find radio transients,” lead author Gerry Zhang, from the University of California, Berkeley, said in a statement. “We hope our success may inspire other serious endeavors in applying machine learning to radio astronomy.”

The new findings show that there is no regular pattern in the data, at least if the pattern is longer than 10 milliseconds. We currently cannot probe shorter periods. The observations, which lasted about five hours, show that the FRB source goes through some quieter periods and some more frenzied periods. The analysis shows that researchers might have underestimated just how many FRB signals might be out there.

“This work is exciting not just because it helps us understand the dynamic behavior of fast radio bursts in more detail, but also because of the promise it shows for using machine learning to detect signals missed by classical algorithms,” added co-author Andrew Siemion, director of the Berkeley SETI Research Center and principal investigator for Breakthrough Listen, the initiative to find signs of intelligent life in the universe.

Breakthrough Listen has a particular interest in these types of algorithms. It could allow the project to become more thorough in finding potential radio signals from alien civilizations.