A deep-learning search for technosignatures of 820 nearby stars

TL;DR

This paper introduces a deep-learning method using a beta-Convolutional Variational Autoencoder to efficiently identify potential extraterrestrial technosignatures in radio telescope data, significantly advancing SETI search capabilities.

Contribution

The study presents the most comprehensive deep-learning based technosignature search, analyzing 820 stars with a novel semi-unsupervised model to improve RFI rejection and candidate identification.

Findings

Identified 8 promising ETI signals for re-observation.

Analyzed over 480 hours of radio data from the Green Bank Telescope.

Demonstrated the effectiveness of a beta-Convolutional Variational Autoencoder in SETI.

Abstract

The goal of the Search for Extraterrestrial Intelligence (SETI) is to quantify the prevalence of technological life beyond Earth via their "technosignatures". One theorized technosignature is narrowband Doppler drifting radio signals. The principal challenge in conducting SETI in the radio domain is developing a generalized technique to reject human radio frequency interference (RFI). Here, we present the most comprehensive deep-learning based technosignature search to date, returning 8 promising ETI signals of interest for re-observation as part of the Breakthrough Listen initiative. The search comprises 820 unique targets observed with the Robert C. Byrd Green Bank Telescope, totaling over 480, hr of on-sky data. We implement a novel beta-Convolutional Variational Autoencoder to identify technosignature candidates in a semi-unsupervised manner while keeping the false positive rate manageably low. This new approach presents itself as a leading solution in accelerating SETI and other transient research into the age of data-driven astronomy.