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

Pinchuk, Pavlo; Margot, Jean-Luc; Greenberg, Adam; Ayalde, Thomas; Bloxham, Chad; Boddu, Arjun; Chinchilla-Garcia, Luis Gerardo; Cliffe, Micah; Gallagher, Sara; Hart, Kira A.; Hesford, Bryan; Mizrahi, Inbal; Pike, Ruth; Rodger, Dominic; Sayki, Bade; Schneck, Una; Tan, Amy; Xiao, Yinxue; Lynch, R.

doi:10.3847/1538-3881/ab0105

Cited by 34 publications

(64 citation statements)

References 16 publications

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“…As a result of these candidate signal detection improvements, we now detect 1.23-1.75 and ∼12 times as many signals as we did with the data processing pipelines of Pinchuk et al (2019) and Margot et al (2018), respectively ( Figure 3). We compare this signal detection performance to that of other searches in Section 6.4.…”

Section: Candidate Signal Detectionmentioning

confidence: 71%

“…Our data acquisition techniques are generally similar to those used by Margot et al (2018) and Pinchuk et al (2019). Here, we give a brief overview and refer the reader to these other works for additional details.…”

Section: Data Acquisition and Pre-processingmentioning

confidence: 99%

“…(Left) Dynamic spectra, where pixel intensity represents signal power. (Middle) Integrated power spectra with blue crosses marking the signals that are detected with the procedure described by Pinchuk et al (2019). In the first scan, the strongest signal (+100 Hz) is detected and the corresponding 5σ bandwidth is shown in red.…”

Section: Candidate Signal Detectionmentioning

confidence: 99%

“…Our search builds on the legacy of technosignature searches performed in the period 1960-2010 (Tarter 2001;Tarter et al 2010, and references therein) and previous searches conducted by our group (Margot et al 2018;Pinchuk et al 2019). Other recent searches include work conducted by Siemion et al (2013); Harp et al (2016); Enriquez et al (2017); Gray & Mooley (2017); Price et al (2020).…”

Section: Introductionmentioning

confidence: 99%

“…Our search algorithms are distinct from the BL algorithms in that they alleviate the necessity of discarding ∼kHz wide regions of frequency space around every detected signal. We abandoned this practice in previous work (Pinchuk et al 2019). In this work, we further refined our algorithm by implementing a candidate signal detection procedure that relies on the concept of prominence and by removing the requirement to compute the bandwidth of candidate signals.…”

Section: Introductionmentioning

confidence: 99%

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A Search for Technosignatures from 14 Planetary Systems in the Kepler Field with the Green Bank Telescope at 1.15–1.73 GHz

Margot¹,

Greenberg²,

Pinchuk³

et al. 2018

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Analysis of Kepler mission data suggests that the Milky Way includes billions of Earth-sized planets in the habitable zone of their host stars. Current technology enables the detection of technosignatures emitted from a large fraction of the Galaxy. We describe a search for technosignatures that is sensitive to Arecibo-class transmitters located within ∼420 ly of Earth and transmitters that are 1000 times more effective than Arecibo within ∼13000 ly of Earth. Our observations focused on 14 planetary systems in the Kepler field and used the L-band receiver (1.15-1.73 GHz) of the 100m diameter Green Bank Telescope. Each source was observed for a total integration time of 5 minutes. We obtained power spectra at a frequency resolution of 3Hz and examined narrowband signals with Doppler drift rates between±9Hzs −1 . We flagged any detection with a signal-to-noise ratio in excess of 10 as a candidate signal and identified approximately 850,000 candidates. Most (99%) of these candidate signals were automatically classified as human-generated radio-frequency interference (RFI). A large fraction (>99%) of the remaining candidate signals were also flagged as anthropogenic RFI because they have frequencies that overlap those used by global navigation satellite systems, satellite downlinks, or other interferers detected in heavily polluted regions of the spectrum. All 19 remaining candidate signals were scrutinized and none were attributable to an extraterrestrial source.

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Section: Candidate Signal Detectionmentioning

confidence: 71%

Section: Data Acquisition and Pre-processingmentioning

confidence: 99%

Section: Candidate Signal Detectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Search for Technosignatures from 14 Planetary Systems in the Kepler Field with the Green Bank Telescope at 1.15–1.73 GHz

Margot¹,

Greenberg²,

Pinchuk³

et al. 2018

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The Breakthrough Listen Search for Intelligent Life: Public Data, Formats, Reduction, and Archiving

Lebofsky

Croft

Siemion

et al. 2019

PASP

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Breakthrough Listen is the most comprehensive and sensitive search for extraterrestrial intelligence (SETI) to date, employing a collection of international observational facilities including both radio and optical telescopes. During the first three years of the Listen program, thousands of targets have been observed with the Green Bank Telescope (GBT), Parkes Telescope and Automated Planet Finder. At GBT and Parkes, observations have been performed ranging from 700 MHz to 26 GHz, with raw data volumes averaging over 1 PB / day. A pseudo-real time software spectroscopy suite is used to produce multi-resolution spectrograms amounting to approximately 400 GB h −1 GHz −1 beam −1 . For certain targets, raw baseband voltage data is also preserved. Observations with the Automated Planet Finder produce both 2-dimensional and 1-dimensional high resolution (R ∼ 10 5 ) echelle spectral data.Although the primary purpose of Listen data acquisition is for SETI, a range of secondary science has also been performed with these data, including studies of fast radio bursts. Other current and potential research topics include spectral line studies, searches for certain kinds of dark matter, probes of interstellar scattering, pulsar searches, radio transient searches and investigations of stellar activity. Listen data are also being used in the development of algorithms, including machine learning approaches to modulation scheme classification and outlier detection, that have wide applicability not just for astronomical research but for a broad range of science and engineering.In this paper, we describe the hardware and software pipeline used for collection, reduction, archival, and public dissemination of Listen data. We describe the data formats and tools, and present Breakthrough Listen Data Release 1.0 (BLDR 1.0), a defined set of publicly-available raw and reduced data totalling 1 PB.

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Narrow-band Signal Localization for SETI on Noisy Synthetic Spectrogram Data

Brzycki

Siemion

Croft

et al. 2020

PASP

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As it stands today, the search for extraterrestrial intelligence is highly dependent on our ability to detect interesting candidate signals, or technosignatures, in radio telescope observations and distinguish these from human radio frequency interference (RFI). Current signal search pipelines look for signals in spectrograms of intensity as a function of time and frequency (which can be thought of as images), but tend to do poorly in identifying multiple signals in a single data frame. This is especially apparent when there are dim signals in the same frame as bright, high signal-to-noise ratio (S/N) signals. In this work, we approach this problem using convolutional neural networks (CNN) as a computationally efficient method for localizing signals in synthetic observations resembling data collected by Breakthrough Listen using the Green Bank Telescope. We generate two synthetic data sets, the first with exactly one signal at various S/N levels and the second with exactly two signals, one of which represents RFI. We find that a residual CNN with strided convolutions and using multiple image normalizations as input outperforms a more basic CNN with max pooling trained on inputs with only one normalization. Training each model on a smaller subset of the training data at higher S/N levels results in a significant increase in model performance, reducing root mean square errors by at least a factor of 3 at an S/N of 25 dB. Although each model produces outliers with significant error, these results demonstrate that using CNNs to analyze signal location is promising, especially in image frames that are crowded with multiple signals.

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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

Cited by 34 publications

References 16 publications

A Search for Technosignatures from 14 Planetary Systems in the Kepler Field with the Green Bank Telescope at 1.15–1.73 GHz

A Search for Technosignatures from 14 Planetary Systems in the Kepler Field with the Green Bank Telescope at 1.15–1.73 GHz

The Breakthrough Listen Search for Intelligent Life: Public Data, Formats, Reduction, and Archiving

Narrow-band Signal Localization for SETI on Noisy Synthetic Spectrogram Data

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