A team from the University of Toronto has developed an algorithm capable of sorting through telescope data to eliminate interference, which they claim will aid in the quest for detecting
"technosignatures" emitted by advanced extraterrestrial civilizations. As a result of using the algorithm, the team has identified eight previously unknown radio signals emanating from five distinct stars.
A machine learning algorithm has discovered eight radio signals from five stars located between 30 to 90 lightyears away.
Although unlikely, there is a possibility that these signals could be "technosignatures" - evidence of advanced extraterrestrial communication. This discovery could mark the first time that such signals have been detected.
University of Toronto scientists have developed an algorithm that uses machine learning to simplify the search for extraterrestrial life by filtering out human-made interference and identifying patterns in deep space signals.
Peter Ma, a Toronto undergraduate and lead author of a new study published in Nature Astronomy, explains that the algorithm is designed to differentiate between uninteresting radio signals from Earth and the more intriguing signals from space.
While the newly discovered signals have characteristics similar to what scientists expect extraterrestrial signals to look like, they have not yet confirmed the presence of aliens. However, they intend to utilize the algorithm on a larger set of radio telescopes to increase their chances of detecting more of these signals.
Cherry Ng, a research associate at Toronto’s Dunlap Institute for Astronomy and Astrophysics, expresses optimism that utilizing artificial intelligence will improve the accuracy of quantifying the possibility of extraterrestrial signals from other civilizations.