A Machine-Learning-Based Direction-of-Origin Filter for the Identification of Radio Frequency Interference in the Search for Technosignatures

Abstract

Radio frequency interference (RFI) mitigation remains a major challenge in the search for radio technosignatures. Typical mitigation strategies include a direction-of-origin (DoO) filter, where a signal is classified as RFI if it is detected in multiple directions on the sky. These classifications generally rely on estimates of signal properties, such as frequency and frequency drift rate. Convolutional neural networks (CNNs) offer a promising complement to existing filters because they can be trained to analyze dynamic spectra directly, instead of relying on inferred signal properties. In this work, we compiled several data sets consisting of labeled pairs of images of dynamic spectra, and we designed and trained a CNN that can determine whether or not a signal detected in one scan is also present in another scan. This CNN-based DoO filter outperforms both a baseline 2D correlation model as well as existing DoO filters over a range of metrics, with precision and recall values of 99.15% and 97.81%, respectively. We found that the CNN reduces the number of signals requiring visual inspection after the application of traditional DoO filters by a factor of 6-16 in nominal situations.

25 pages, 14 figures, in press at the Astronomical Journal (submitted July 28, 2021; review received October 21, 2021; resubmitted October 25, 2021; accepted December 7, 2021)

References in corpus (8)

• Array Programming with NumPy
• Machine Learning in Astronomy: a practical overview
• The Breakthrough Listen Search For Intelligent Life Near the Galactic Center I
• A Search for Technosignatures Around 31 Sun-like Stars with the Green Bank Telescope at 1.15-1.73 GHz
• A search for technosignatures from TRAPPIST-1, LHS 1140, and 10 planetary systems in the Kepler field with the Green Bank Telescope at 1.15-1.73 GHz
• The Breakthrough Listen Search for Intelligent Life: Searching for Technosignatures in Observations of TESS Targets of Interest
• Narrow-Band Signal Localization for SETI on Noisy Synthetic Spectrogram Data
• Machine Vision and Deep Learning for Classification of Radio SETI Signals

Cited by in corpus (11)

• A deep-learning search for technosignatures of 820 nearby stars
• Searching for broadband pulsed beacons from 1883 stars using neural networks
• Setigen: Simulating Radio Technosignatures for SETI
• A Search for Technosignatures Around 11,680 Stars with the Green Bank Telescope at 1.15-1.73 GHz
• Application of Deep Learning Methods for Distinguishing Gamma-Ray Bursts from Fermi/GBM TTE Data
• Anomaly Detection and Radio-frequency Interference Classification with Unsupervised Learning in Narrowband Radio Technosignature Searches
• A solution to continuous RFI in narrowband radio SETI with FAST: The MultiBeam Point-source Scanning strategy
• On Detecting Interstellar Scintillation in Narrowband Radio SETI
• A Deep Neural Network Based Reverse Radio Spectrogram Search Algorithm
• SETI in 2022
• Searching for technosignatures in exoplanetary systems with current and future missions