Announcement about the detection of FRB-121102 was first made last year, and the detection was credited to Dr Vishal Gajjar, a UC Berkeley Postdoctoral Researcher. He originally hails from Gujarat. Fast radio bursts, or FRBs, are brief (just milliseconds in duration), bright pulses of radio emission from distant galaxies. First detected with the Parkes Telescope in Australia, FRBs have now been seen by several radio telescopes around the world.
On Monday, Breakthrough said: “Most FRBs have been witnessed during just a single outburst. In contrast, FRB-121102 is the only one to date known to emit repeated bursts, including 21 detected during Breakthrough Listen observations made in 2017 with the Green Bank Telescope (GBT) in West Virginia.”
Dr Vishal Gajjar, a Berkeley Postdoctoral Researcher is working on the Breakthrough Listen project (Photo credit: UC Berkeley)
Pete Worden, the executive director of Breakthrough Initiatives said that not all discoveries come from new observations, while adding that in this case, it was smart, original thinking applied to an existing dataset.
“This has advanced our knowledge of one of the most tantalizing mysteries in astronomy,” he said. The Listen science team, which, after a five-hour long observation led by Gajjar last year had detected the FRB-121102 in 2017, has now developed a new, powerful machine learning algorithm which has reanalysed the 2017 dataset which has led to the 72 new FRBs.
Gerry Zhang, a UC Berkeley PhD student and collaborators have developed this algorithm. The team used some of the same techniques that internet technology companies use to optimize search results and classify images.
“They trained an algorithm known as a convolutional neural network to recognize bursts found with the classical search method used by Gajjar and collaborators, and then set it loose on the 400TB dataset to find bursts that the classical approach missed,” Breakthrough said.
The results have helped put new constraints on the periodicity of the pulses from FRB-121102, suggesting that the pulses are not received with a regular pattern. Also, 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.
“This work is only the beginning of using these powerful methods to find radio transients. We hope our success may inspire other serious endeavors in applying machine learning to radio astronomy,” Zhang said.
Gerry’s work not just helps scientists understand the dynamic behavior of FRBs in more detail, but also shows promise for using machine learning to detect signals missed by classical algorithms.
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.