Self-supervised Anomaly Detection for Narrowband SETI

The Search for Extra-terrestrial Intelligence (SETI) aims to find technological signals of extra-solar origin. Radio frequency SETI is characterized by large unlabeled datasets and complex interference environment. The infinite possibilities of potential signal types require generalizable signal pro...

Full description

Saved in:
Bibliographic Details
Published inarXiv.org
Main Authors Zhang, Yunfan Gerry, Won, Ki Hyun, Seung Woo Son, Siemion, Andrew, Croft, Steve
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 15.01.2019
Subjects
Anomalies
Intelligence
Machine learning
Narrowband
Signal processing
Spatial filtering
Spectrograms
Online AccessGet full text

Cover

More Information
Summary:The Search for Extra-terrestrial Intelligence (SETI) aims to find technological signals of extra-solar origin. Radio frequency SETI is characterized by large unlabeled datasets and complex interference environment. The infinite possibilities of potential signal types require generalizable signal processing techniques with little human supervision. We present a generative model of self-supervised deep learning that can be used for anomaly detection and spatial filtering. We develop and evaluate our approach on spectrograms containing narrowband signals collected by Breakthrough Listen at the Green Bank telescope. The proposed approach is not meant to replace current narrowband searches but to demonstrate the potential to generalize to other signal types.
ISSN:2331-8422