In a paper published in the journal Nature Astronomythe team analyse 480 hours of data from the Green Bank Telescope (GBT) in West Virginia, and report eight previously undetected signals of interest that have certain characteristics expected of genuine technosignatures.
The research, led by University of Toronto undergraduate student Peter Mawho began working with the Breakthrough Listen team while still in high school, identified around 3 million signals in scans of 820 stars observed with GBT.
“The key issue with any techno-signature search is looking through this huge haystack of signals to find the needle that might be a transmission from an alien world,” explained Steve Croft, an astrophysicist with the Breakthrough Listen team at the University of California, Berkeley (and one of Ma’s research advisors).
Croft added: “Vast majority of the signals detected by our telescopes originate from our own technology — GPS satellites, mobile phones, and the like. Peter’s algorithm gives us a more effective way to filter the haystack and find signals that have the characteristics we expect from technosignatures.”
Classical techno-signature algorithms compare scans where the telescope is pointed at a target point on the sky with scans where the telescope moves to a nearby position, in order to identify signals that may be coming from only that specific point, Breakthrough added.
These techniques, it said, are highly effective. For example, they can successfully identify the Voyager-1 space probe, at a distance of 20 billion kilometres, in observations with the GBT. But these algorithms struggle in crowded regions of the radio spectrum, where the challenge is akin to listening for a whisper in a crowded room.
“The process developed by Ma inserts simulated signals into real data, and trains an artificial intelligence algorithm known as an autoencoder to learn their fundamental properties. The output from this process is fed into a second algorithm known as a random forest classifier, which learns to distinguish the candidate signals from the noisy background,” Breakthrough said.
Andrew Siemion, Breakthrough Listen’s Principal Investigator, said that in 2021 their classical algorithms uncovered a signal of interest, denoted BLC1, in data from the Parkes telescope and that Peter’s algorithm was even more effective in finding signals like this.
“Any techno-signature candidate needs to be confirmed, however, and when we looked at these targets again with the GBT, the signals did not re-appear. But by applying this new technique to even larger datasets, we can more effectively identify techno-signature candidates, and hopefully eventually even a confirmed techno-signature,” Siemion added.
Breakthrough Initiatives Executive Director S Pete Worden said it was exciting to see new approaches being developed by imaginative young people at the beginning of their scientific careers.
“We’ll continue to monitor the stars Peter observed, and we’ll continue to develop our use of artificial intelligence to help us try to answer humanity’s most profound question: are we alone?” Worden said.
Cherry Ng, another of Ma’s research advisors at the University of Toronto and now an astronomer at the French National Center for Scientific Research said these results dramatically illustrate the power of applying modern machine learning and computer vision methods to data challenges in astronomy, resulting in both new detections and higher performance.
“…Application of these techniques at scale will be transformational for radio techno-signature science,” Ng added.
Stating that larger datasets were imminent, Breakthrough added how it recently announced the start of observations using the MeerKAT telescope in South Africa.
“There is such a large volume of data. The more traditional ways of searching for extraterrestrial life are just not sufficient,” explains Ma.