SETI, the search for extraterrestrial intelligence, is deploying machine-learning algorithms that filter out Earthly interference and spot signals humans might miss.
“The biggest challenge for us in looking for SETI signals is not at this point getting the data,” says Sofia Sheikh, an astronomer at the SETI Institute. “The difficult part is differentiating signals from human or Earth technology from the kind of signals we’d be looking for from technology somewhere else out in the Galaxy.”
Enter machine learning. Machine-learning algorithms are trained on large amounts of data and can learn to recognize features that are characteristic of Earthly interference, making them very good at filtering out the noise.Machine learning is also good at picking up candidate extraterrestrial signals that don’t fall into conventional categories and so might have been missed by earlier methods, says Dan Werthimer, a SETI scientist at the University of California, Berkeley.
Ma and his colleagues sifted through Breakthrough Listen observations of 820 stars, made using the 100-metre Robert C. Byrd Green Bank Telescope. They built machine-learning software to analyse the data, which netted nearly three million signals of interest but discarded most as Earth-based interference. Ma then manually reviewed around 20,000 signals and narrowed them down to 8 intriguing candidates.
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