Eight Signals from Distant Stars Found Promising in the Search for Extraterrestrial Life Using New Method. Utilizing a new algorithm, scientists have detected 8 extraterrestrial signals that seem to bear the hallmark of technology.
The research team, led by experts from the University of Toronto, employed a new algorithm to sort through data from telescopes, distinguishing between extraterrestrial signals and artificial signals.
This advancement allowed them to quickly process information through a machine learning approach. The authors of the study believe that artificial intelligence will enhance the ability to search for signals from other civilizations.
Discovery of Eight Extraterrestrial Signals
Radio telescopes scanning the universe for signs of extraterrestrial intelligence.
Cherry Ng, an astronomer at the University of Toronto, Canada, and co-author of the study stated: “I am very impressed by how effectively this approach has performed in the search for extraterrestrial intelligence. With the help of artificial intelligence, I am optimistic that we will be able to better quantify the likelihood of detecting signals from other civilizations.”
Initially, the researchers designed the algorithm to differentiate between signals generated by humans from radio waves originating from Earth and radio signals coming from elsewhere. (Radio waves are a common target in the search for extraterrestrial intelligence, or SETI, because they can travel long distances through space).
The researchers experimented with various algorithms to minimize false positive results. They analyzed 150 terabytes of data from the Green Bank Telescope in West Virginia, which included observations of 820 stars near Earth. Subsequently, they discovered eight previously overlooked signals from five stars located between 30 to 90 light-years away from Earth.
Scientists from Breakthrough Listen noted that these signals share two characteristics with signals potentially produced by intelligent extraterrestrials.
“First, they are present when we look at the star and absent when we look elsewhere—contrary to local interference, which is usually ever-present. Second, the signals change frequency over time in a way that makes them appear distant from the telescope,” said Steve Croft, project scientist for Breakthrough Listen at the Green Bank Telescope.
However, these features might arise coincidentally. Before making any claims about extraterrestrial life, the researchers will need to observe similar signals repeatedly.
The research team hopes to apply their algorithm to data from more powerful radio telescopes, such as MeerKAT in South Africa, which is planned and located across North America.
The lead researcher of this project stated: “With our new technique, combined with the next generation of telescopes, we hope that machine learning technology can take us from searching hundreds of stars to millions of stars.”
Detection of Extraterrestrial Signals Using AI
To distinguish extraterrestrial signals from human-made radio waves, the team trained their machine learning tools through simulations of both signal types. They tested various algorithms, evaluated their accuracy, and ultimately selected a robust algorithm created by Peter Ma.
The researchers first designed the algorithm to differentiate between human-generated signals from radio waves originating from Earth and radio signals coming from elsewhere.
Ma’s algorithm combines both supervised and unsupervised machine learning techniques. This combination enables the algorithm to generalize information, resulting in better outcomes in the search for extraterrestrial signals.
Peter Ma, the creator of the algorithm that accelerates the detection of extraterrestrial signals. (Photo: Adar Kahiri).
Peter Ma revealed that the idea for this algorithm originated from a high school project and was not particularly appreciated by his teachers.
Nonetheless, Dr. Ng emphasized that new ideas are crucial in a field like SETI. “By scanning all data with a new technique, we can uncover interesting signals.”
The research team hopes to apply their algorithm to data from stronger radio telescopes, such as MeerKAT in South Africa.
“With our new technique, combined with the next generation of telescopes, we hope that machine learning technology can take us from searching hundreds of stars to millions of stars,” Ma commented.
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