More Data and Machine Learning has Kicked SETI Into High Gear universetoday storybywill
Narrowband radio signals remain the most popular and sought-after technosignature because of how well radio waves propagate in interstellar space. For SETI researchers, the challenge has always been distinguishing between possible transmissions, terrestrial RFI, and radio waves from cosmic sources. For the sake of their study, Ma and his colleagues applied a Beta-Convolutional Variational Autoencoder algorithm to 820 unique targets observed by the GBT during 480 hours of on-sky observation.
Since joining the Dunlap Institute, Ma and his colleagues have trained the algorithm using simulated signals to differentiate between potential signals that could be extraterrestrial in origin and human-generated interference. They also compared Ma’s algorithm to various machine learning applications, their precision and false-positive rates, and used that information to create the finished product.
When dealing with datasets containing millions of signals, some signals can have the two characteristics just by sheer chance. For this reason, the researchers are not yet convinced that the signals they observed represent extraterrestrial transmissions, even though they appear the way the team would expect. For starters, the signals were not found when the team conducted follow-up observations with the GBT of the same stars.
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