The scientific community faces serious difficulties when it comes to examining outer space and picking up signs of possible extraterrestrial lifeDue to the abundance of data and the multiplicity of instruments used to search for such signals. However, the application of machine learning and recent findings of artificial intelligence would have changed the course of the investigations: indeed, they detected eight signs using the Robert C. Byrd Green Bank radio telescope, a discovery that could indicate the presence of extraterrestrial civilizations in the observed area.
The breakthroughs are attributed to research conducted by University of Toronto undergraduate Peter Ma, in conjunction with the Search Institute for Extraterrestrial Intelligence (SETI), Breakthrough Listen, and scientific research institutions around the world. The novelty consisted of apply machine learning and artificial intelligence to a previously studied dataset of nearby starsas specified on the agency site DW.
After applying the ingenious method, they discovered “eight signs of interest” previously unidentified, according to a press release.
Initial results of new research published in Nature Astronomy indicate that there would be “great chances” that the new method has brought to light non-terrestrial “technosignatures”. This would confirm that the objective of SETI, which was to find signs of extraterrestrial intelligence.
How the 8 “signs of interest” were discovered
The new system applied by the scientists detected Eight “Interesting Enough” Signals to Drive Follow-Up Trades a posteriori. The study referenced a small portion of the radio telescope recordings.
“In total, we had searched through 150TB of data from 820 nearby starsin a dataset previously searched in 2017 using classical techniques, but labeled as lacking interesting signals,” Peter Ma said in remarks quoted in DW.
“We are scaling this search effort to 1 million stars today with the MeerKAT telescope and beyond. We believe work like this will help accelerate the rate at which we are able to make discoveries in our great effort to answer the question ‘are we alone in the universe?‘” said the specialist.
The data comes from 480 hours of observations of 820 stars made by the Robert C. Byrd Green Bank radio telescope, contracted by SETI Breakthrough to search for radio waves that could be an indication of the presence of extraterrestrial civilizations.
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Ma’s algorithm specifically selected the eight radio signals because, among other factors, they are of Narrow band. According to the press release issued by the SETI Institute, “signals caused by natural phenomena tend to be broadband.”
The detected signals also showed a behavior and series of properties suggesting that they are not caused by terrestrial interferencestarting with the fact that they had non-zero drift rates.
This means, according to the researchers, that the signals had a slope, which could indicate that the origin of a signal had some relative acceleration with the receivers, so it was not local to the radio observatory.
“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 increased throughput. Applying these techniques at scale will be transformative for the science of radio technosignatures.said Cherry Ng, one of Peter Ma’s research advisers and an astronomer at both the SETI Institute and the French National Center for Scientific Research.
DW/CA/DS
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