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.
@article{arxiv.2301.12670,
title = {A deep-learning search for technosignatures of 820 nearby stars},
author = {Peter Xiangyuan Ma and Cherry Ng and Leandro Rizk and Steve Croft and Andrew P. V. Siemion and Bryan Brzycki and Daniel Czech and Jamie Drew and Vishal Gajjar and John Hoang and Howard Isaacson and Matt Lebofsky and David MacMahon and Imke de Pater and Danny C. Price and Sofia Z. Sheikh and S. Pete Worden},
journal= {arXiv preprint arXiv:2301.12670},
year = {2023}
}
Comments
10 pages of main paper followed by 16 pages of methods; 17 figures total and 7 tables; published in Nature Astronomy