Astronomers at Breakthrough Listen, a scientific program in search for signs of intelligent life in the Universe, have applied machine-learning techniques to discover 72 new pulses from FRB 121102, a mysterious source some 3 billion light-years from Earth. The new results will be published in the Astrophysical Journal.
Zhang et al used artificial intelligence to search through radio signals recorded from FRB 121102. Image credit: Breakthrough Listen Initiative.
Fast radio bursts (FRBs) are mysterious and rarely detected bursts of energy from space.
These events have durations of milliseconds and exhibit the characteristic dispersion sweep of radio pulsars.
They emit as much energy in one millisecond as the Sun emits in 10,000 years, but the physical phenomenon that causes them is unknown.
Theories range from highly magnetized neutron stars, blasted by gas streams near to a supermassive black hole, to suggestions that the burst properties are consistent with signatures of technology developed by an advanced civilization.
Discovered by Arecibo Observatory astronomers in November 2012, FRB 121102 is the only one known to repeat. More than 200 high-energy bursts have been observed coming from this source.
“Not all discoveries come from new observations. In this case, it was smart, original thinking applied to an existing dataset. It has advanced our knowledge of one of the most tantalizing mysteries in astronomy,” said Dr. Pete Worden, executive director of the Breakthrough Initiatives.
In search of a deeper understanding of this intriguing object, the Breakthrough Listen astronomers observed FRB 121102 for five hours on August 26, 2017, using the Green Bank Telescope in West Virginia.
Combing through 400 TB of data, they found a total of 21 bursts. All were seen within one hour, suggesting that the source alternates between periods of quiescence and frenzied activity.
They subsequently developed a new machine-learning algorithm and reanalyzed the 2017 data, finding an additional 72 bursts not detected originally. This brings the total number of detected bursts from FRB 121102 to around 300.
“This work is only the beginning of using these powerful methods to find radio transients,” said Gerry Zhang, a Ph.D. student at the University of California, Berkeley.
“We hope our success may inspire other serious endeavors in applying machine learning to radio astronomy.”
The team’s 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 (at least if the period of that pattern is longer than about 10 milliseconds).
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 exciting not just because it helps us understand the dynamic behavior of FRBs in more detail, but also because of the promise it shows for using machine learning to detect signals missed by classical algorithms,” said Berkeley SETI Research Center Director and Breakthrough Listen Principal Investigator Dr. Andrew Siemion.
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Yunfan Gerry Zhang et al. 2018. Fast Radio Burst 121102 Pulse Detection and Periodicity: A Machine Learning Approach. ApJ, in press; arXiv: 1809.03043
