The first deep-learning search for radio technosignatures from 820 nearby stars
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) that dominates the features across the band in searches for technosignatures. Here, we will present the first 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 480hr of on-sky data. We implement a novel beta-Convolutional Variational Autoencoder with an embedded discriminator combined with Random Forest Decision Trees to classify technosignature signals of interest in a semi-unsupervised manner. We compare our results with prior classical techniques on the same dataset and conclude that our algorithm returns more convincing signals of interest with a manageable false positive rate. This novel approach presents itself as a leading solution in accelerating SETI and other transient research into the new age of data-driven astronomy.
The project was supported by the Laidlaw foundation which has funded this project as part of the undergraduate research and leadership funding initiative.- Publication:
-
APS April Meeting Abstracts
- Pub Date:
- April 2022
- Bibcode:
- 2022APS..APRB09008M