For more than 60 years, scientists have been searching the cosmos for possible signs of radio transmission that would indicate the existence of extraterrestrial intelligence (ETI). In that time, the technology and methods have matured considerably, but the greatest challenges remain. In addition to having never detected a radio signal of extraterrestrial origin, there is a wide range of possible forms that such a broadcast could take.
In short, SETI researchers must assume what a signal would look like, but without the benefit of any known examples. Recently, an international team led by the University of California Berkeley and the SETI Institute developed a new machine learning tool that simulates what a message from extraterrestrial intelligence (ETI) might look like. It's known as Setigen, an open-source library that could be a game-changer for future SETI research.
The research team was led by Bryan Brzycki, an astronomy graduate student at UC Berkeley. He was joined by Andrew Siemion, the Director of the Berkeley SETI Research Center, and researchers from the SETI Institute, Breakthrough Listen, the Dunlap Institute for Astronomy & Astrophysics, the Institute of Space Sciences and Astronomy, International Center for Radio Astronomy Research (ICRAR), and the Goergen Institute for Data Science.
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