Robert Lea is a science journalist in the U.K. whose articles have been published in Physics World, New Scientist, Astronomy Magazine, All About Space, Newsweek and ZME Science. He also writes about science communication for Elsevier and the European Journal of Physics. Rob holds a bachelor of science degree in physics and astronomy from the U.K.’s Open University. Follow him on Twitter @sciencef1rst.
This isn't the first time that computer algorithms have been used to search the vastness of space for"technosignatures," technologically-generated signals that could mark other advanced extraterrestrial civilizations.
The researchers found that the key strength of the new algorithm was to organize the data from telescopes into categories, allowing them to distinguish between real signals and"noise," or interference. Although telescopes involved in the search for technosignatures are placed in areas of the globe where there is minimal interference from human technology like cell phones, these signals still get picked up.
The most successful algorithm combined two subtypes of machine learning: supervised learning, in which humans train the algorithm to help it generalize, and unsupervised learning that can hunt through large data sets for new hidden patterns. United in what Ma called"semi-unsupervised learning," these approaches discovered eight signals that originated from five different stars located between 30 and 90 light-years away from Earth.
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