Machine learning algorithms applied to Breakthrough Listen data from the Green Bank Telescope have been used to find signals of interest, while filtering out radio frequency interference, as reported in a new paper accepted for publication in Nature Astronomy. Follow-up observations did not re-detect the signals, so they do not pass the criteria required for bona fide technosignature candidates. However, the technique represents a promising new way to look for anomalies in data from our searches, while efficiently filtering out millions of signals from our own technology.
A paper describing these results (see also shareable PDF link) has been accepted for publication in the journal Nature Astronomy
and a preprint is also available.
Read the press releases from the
University of Toronto,
the SETI Institute,
and
the Breakthrough Prize Foundation.
You can clone the analysis code at github. This repository also contains full target lists and data for the top candidates noted in the paper.
Access the full data set used in the search in our Open Data Archive (technical and programming skills required).
Download the background art seen here (Credit: Breakthrough Listen / Danielle Futselaar):
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medium resolution .jpg -
low resolution .jpg