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.

Since the 1960s, the most common method of SETI has involved searching the cosmos for radio signals that are artificial in origin. The first such experiment was Project Ozma (April to July 1960), led by famed Cornell astrophysicist Frank Drake (creator of the Drake Equation). This survey relied on the 25-meter dish at the National Radio Astronomy Observatory in Green Bank, West Virginia, to monitor Epsilon Eridani and Tau Ceti at frequencies of about 400 kHz around 1.42 GHz.

These searches have since expanded to cover larger areas of the night sky, wider frequency ranges, and greater signal diversity. As Brzycki explained to Universe Today via email: