A machine learning algorithm designed to hunt for extraterrestrial intelligence has identified eight new signals of interest.
The algorithm, developed by Peter Xiangyuan Ma at the University of Toronto in Canada, and his team, was trained to analyze vast datasets from radio telescopes, specifically looking for signals that could not have a terrestrial origin.
The Search for Extraterrestrial Intelligence (SETI) project, which includes the Breakthrough Listen Initiative, has been combing through massive data sets, looking for signs of technologically advanced civilizations elsewhere in the galaxy. The challenge lies in filtering out the tens of millions of false positives found in these datasets. The machine learning algorithm developed by Xiangyuan Ma and his team has been trained to identify distinctive patterns in the data, making it easier to spot potential signals of interest.
The team used the algorithm to analyze 480 hours of observations of 820 stars taken by the Robert C. Byrd Green Bank Telescope in West Virginia. The algorithm identified almost 3 million distinctive patterns, which were then further filtered down to just 20,515. Upon visual inspection, the team identified eight promising signals of interest.
These signals correspond to five stars located between 30 and 90 light-years from Earth. One of these stars, HIP 62207, is similar to our sun. However, the signals were not persistent in time, disappearing when the stars were reobserved. Despite this, the team encourages further observations of these targets.
This work represents the most comprehensive machine learning-based technosignature search to date. The use of automated analyses like this one will make future studies more manageable and could improve our capabilities for discovering other civilizations. While the origin of these signals remains uncertain, their discovery opens up new possibilities for understanding the universe.