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Abstract:

The Breakthrough Listen program is, to date, the most extensive search for technological life beyond Earth. Over the past 9 yr, it has surveyed thousands of nearby stars and close to 100 nearby galaxies with telescopes around the world, including the Robert C. Byrd Green Bank Telescope (GBT) in West Virginia. The goal is to find evidence of technosignatures of other civilizations, such as narrowband Doppler-drifting radio signals. Despite the GBT’s location in a radio-quiet zone, the primary challenge of this search continues to be the ability to pick out genuine candidates from the high quantities of human-generated radio-frequency interference (RFI). Here we present a novel search method aimed at finding these “needle-in-a-haystack”-type signals, applied to 9684 observation cadences of 3077 stars (each observed with one or more of the L-, S-, C-, and X-band receivers) from the GBT archive. We implement a low-complexity statistical process to vet out RFI and highlight signals that, upon visual inspection, are less evidently RFI than those from previous analyses. Our work returns candidate signals found previously using both traditional and machine learning algorithms, as well as many not previously identified. This analysis represents the largest data set searched for technosignatures to date, and highlights the efficacy that traditional algorithms continue to have in these types of technosignature searches. We find that less than 1% of stars host transmitters brighter than ∼0.3 Arecibo radar equivalents broadcasting in our direction over the frequency band covered.