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A Search for Technosignatures Around 11,680 Stars with the Green Bank Telescope at 1.15–1.73 GHz

2023/08/04 by Jean-Luc Margot, Jean‐Luc Margot, Megan G. Li +87 · 3 voices · 2 citations
Physics and Astronomy · #Space Science and Extraterrestrial Life #Gamma-ray bursts and supernovae #Planetary Science and Exploration

paper · pdf · doi:10.3847/1538-3881/acfda4

Abstract

Abstract We conducted a search for narrowband radio signals over four observing sessions in 2020–2023 with the L -band receiver (1.15–1.73 GHz) of the 100 m diameter Green Bank Telescope. We pointed the telescope in the directions of 62 TESS Objects of Interest, capturing radio emissions from a total of ∼11,680 stars and planetary systems in the ∼9′ beam of the telescope. All detections were either automatically rejected or visually inspected and confirmed to be of anthropogenic nature. We also quantified the end-to-end efficiency of radio SETI pipelines with a signal injection and recovery analysis. The UCLA SETI pipeline recovers 94.0% of the injected signals over the usable frequency range of the receiver and 98.7% of the injections when regions of dense radio frequency interference are excluded. In another pipeline that uses incoherent sums of 51 consecutive spectra, the recovery rate is ∼15 times smaller at ∼6%. The pipeline efficiency affects calculations of transmitter prevalence and SETI search volume. Accordingly, we developed an improved Drake figure of merit and a formalism to place upper limits on transmitter prevalence that take the pipeline efficiency and transmitter duty cycle into account. Based on our observations, we can state at the 95% confidence level that fewer than 6.6% of stars within 100 pc host a transmitter that is continuously transmitting a narrowband signal with an equivalent isotropic radiated power (EIRP) > 10 13 W. For stars within 20,000 ly, the fraction of stars with detectable transmitters (EIRP > 5 × 10 16 W) is at most 3 × 10 −4 . Finally, we showed that the UCLA SETI pipeline natively detects the signals detected with AI techniques by Ma et al.

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