Machine Vision and Deep Learning for Classification of Radio SETI Signals

Understand

We apply classical machine vision and machine deep learning methods to prototype signal classifiers for the search for extraterrestrial intelligence.

• Our novel approach uses two-dimensional spectrograms of measured and simulated radio signals bearing the imprint of a technological origin.
• The studies are performed using archived narrow-band signal data captured from real-time SETI observations with the Allen Telescope Array and a set of digitally simulated signals designed to mimic real observed signals.
• By treating the 2D spectrogram as an image, we show that high quality parametric and non-parametric classifiers based on automated visual analysis can achieve high levels of discrimination and accuracy, as well as low false-positive rates.