A Novel Technosignature Search in the Breakthrough Listen Green Bank Telescope Archive

TL;DR

This paper presents a new method for searching for extraterrestrial technosignatures in the Green Bank Telescope archive, successfully identifying promising signals and demonstrating the continued effectiveness of traditional algorithms in large-scale radio SETI searches.

Contribution

Introduces a novel, low-complexity statistical search method applied to the largest dataset of GBT observations for technosignatures, highlighting the value of traditional algorithms.

Findings

Less than 1% of stars host detectable transmitters.

The method identified previously known and new candidate signals.

Traditional algorithms remain effective in large-scale searches.

Abstract

The Breakthrough Listen program is, to date, the most extensive search for technological life beyond Earth. As part of this goal, over the past nine years it has surveyed thousands of nearby stars, close to 100 nearby galaxies, and a variety of exotic and solar system objects 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 high quantities of human-generated radio-frequency interference (RFI), and the ability to pick out genuinely promising candidates from it. Here we present a novel search method aimed at finding these `needle-in-a-haystack' type signals, applied to 6,630 observation cadences of 2,623 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, appear more promising than those from previous analyses. Our work returns candidate signals found previously using both traditional and machine learning algorithms, as well as many promising ones not previously identified. This analysis represents the largest dataset searched for technosignatures to date, and highlights the efficacy that traditional (non-machine-learning) 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.