A Search for Technosignatures around 31 Sun-like Stars with the Green Bank Telescope at 1.15–1.73 GHz

Abstract

We conducted a search for technosignatures in 2018 and 2019 April with the L-band receiver (1.15–1.73 GHz) of the 100 m diameter Green Bank Telescope. These observations focused on regions surrounding 31 Sun-like stars near the plane of the Galaxy. We present the results of our search for narrowband signals in this data set, as well as improvements to our data processing pipeline. Specifically, we applied an improved candidate signal detection procedure that relies on the topographic prominence of the signal power, which nearly doubles the signal detection count of some previously analyzed data sets. We also improved the direction-of-origin filters that remove most radio frequency interference (RFI) to ensure that they uniquely link signals observed in separate scans. We performed a preliminary signal injection and recovery analysis to test the performance of our pipeline. We found that our pipeline recovers 93% of the injected signals over the usable frequency range of the receiver and 98% if we exclude regions with dense RFI. In this analysis, 99.73% of the recovered signals were correctly classified as technosignature candidates. Our improved data processing pipeline classified over 99.84% of the ∼26 million signals detected in our data as RFI. Of the remaining candidates, 4539 were detected outside of known RFI frequency regions. The remaining candidates were visually inspected and verified to be of anthropogenic nature. Our search compares favorably to other recent searches in terms of end-to-end sensitivity, frequency drift rate coverage, and signal detection count per unit bandwidth per unit integration time.

The Breakthrough Listen Search for Intelligent Life: Technosignature Search of Transiting TESS Targets of Interest

“…where n is the number of observations at a receiver, ∆f is the total frequency range observed, Ω is the full width half maximum of the receiver, and F min is the minimum detectable flux. While DFM has some limitations, as discussed by Enriquez et al (2017) and Margot et al (2021), it is still a useful statistic, especially for surveys across multiple receivers, such as this one, because it incorporates both the bandwidth surveyed and the minimum detectable power. 1.…”

The Breakthrough Listen Search for Intelligent Life: Searching for Technosignatures in Observations of TESS Targets of Interest

“…The fine-frequency resolution data products, with a channelization of 2.7 Hz (Lebofsky et al 2019) TurboSETI is a Doppler drift search algorithm designed to search for narrowband drifting signals. Detecting narrowband signals in regions crowded with RFI, and over wide ranges of Doppler drift rates, is challenging (see, e.g., Margot et al 2021), and detection efficiencies for certain kinds of signals may be lower than expected (including for signals that are more complex than the simple assumption of a narrowband tone). Work is ongoing to enhance the capabilities of turboSETI, including better handling of signals spread across multiple frequency channels, and quantifying detection efficiency using signal injection and recovery 5 .…”

Setigen: Simulating Radio Technosignatures for SETI

“…INTRODUCTION Since the inception of radio SETI in the 1960s, technosignature searches have greatly expanded to cover more sky area, wider frequency ranges, and a larger variety of signal morphologies (Drake 1961;Werthimer et al 1985;Tarter 2001;Siemion et al 2013;Wright et al 2014;MacMahon et al 2018;Price et al 2018;Gajjar et al 2021). Arguably the most developed branch of radio SETI is the search for narrow-band technosignatures, with signal bandwidths under 1 kHz, for which search algorithms are constantly being produced and improved (Siemion et al 2013;Enriquez et al 2017;Margot et al 2021). These algorithms operate on either voltage time series data or time-frequency spectrogram data (i.e., dynamic spectra, waterfall plots).…”

“…The incoherent tree deDoppler method is the primary search strategy for Doppler-accelerated narrow-band signals in radio spectrograms (Taylor 1974;Siemion et al 2013;Enriquez et al 2017;Margot et al 2021). An ideal sinusoidal emitter will appear to exhibit a frequency drift over time due to relative acceleration between the emitter and receiving telescope (Sheikh et al 2019).…”

“…For an ML-based direction-of-origin filter, used a separate non-ML method to detect signals and create an algorithmicallylabeled spectrogram dataset. In most cases, however, supervised approaches have relied on generating synthetic signals of various classes in order to guarantee correct labels (Harp et al 2019;Brzycki et al 2020;Margot et al 2021).…”