The Astrophysical Journal · 2022 · 25 citations · 42 references
Natural SciencesAstronomyComputer ScienceAstrophysicsMachine LearningRadio AstronomyComputational AstrophysicsDeep LearningRadio TelescopesStellar ClassificationAstronomical SourcesConvolutional Neural Network DetectionConvolutional Neural NetworksDeep Field ObservationsExtraterrestrial LifeHigh-performance Computing Performance
Abstract The search for extraterrestrial intelligence at radio frequencies has largely been focused on continuous-wave narrowband signals. We demonstrate that broadband pulsed beacons are energetically efficient compared to narrowband beacons over longer operational timescales. Here, we report the first extensive survey searching for such broadband pulsed beacons toward 1883 stars as a part of the Breakthrough Listen’s search for advanced intelligent life. We conducted 233 hr of deep observations across 4–8 GHz using the Robert C. Byrd Green Bank Telescope and searched for three different classes of signals with artificial (or negative) dispersion. We report a detailed search—leveraging a convolutional neural network classifier on high-performance GPUs—deployed for the very first time in a large-scale search for signals from extraterrestrial intelligence. Due to the absence of any signal-of-interest from our survey, we place a constraint on the existence of broadband pulsed beacons in our solar neighborhood: ≲1 in 1000 stars have transmitter power densities ≳10 5 W Hz −1 repeating ≤500 s at these frequencies.
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