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

Authors

Pavlo Pinchuk, University of California, Los Angeles
Jean Luc Margot, University of California, Los Angeles
Adam H. Greenberg, University of California, Los Angeles
Thomas Ayalde, University of California, Los Angeles
Chad Bloxham, University of California, Los Angeles
Arjun Boddu, University of California, Los Angeles
Luis Gerardo Chinchilla-Garcia, University of California, Los Angeles
Micah Cliffe, University of California, Los Angeles
Sara Gallagher, University of California, Los Angeles
Kira Hart, University of California, Los Angeles
Brayden Hesford, University of California, Los Angeles
Inbal Mizrahi, University of California, Los Angeles
Ruth Pike, Imperial College London
Dominic Rodger, Imperial College London
Bade Sayki, University of California, Los Angeles
Una Schneck, University of California, Los Angeles
Aysen Tan, University of California, Los Angeles
Yinxue Yolanda Xiao, University of California, Los Angeles
Ryan S. Lynch, Green Bank Observatory

Document Type

Article

Publication Date

1-1-2019

Journal

Astronomical Journal

Volume

157

Issue

3

URL with Digital Object Identifier

10.3847/1538-3881/ab0105

Abstract

As part of our ongoing search for technosignatures, we collected over three terabytes of data in 2017 May with the L-band receiver (1.15-1.73 GHz) of the 100 m diameter Green Bank Telescope. These observations focused primarily on planetary systems in the Kepler field, but also included scans of the recently discovered TRAPPIST-1 and LHS 1140 systems. We present the results of our search for narrowband signals in this data set with techniques that are generally similar to those described by Margot et al. Our improved data processing pipeline classified over 98% of the approximately six million detected signals as anthropogenic radio frequency interference (RFI). Of the remaining candidates, 30 were detected outside of densely populated frequency regions attributable to RFI. These candidates were carefully examined and determined to be of terrestrial origin. We discuss the problems associated with the common practice of ignoring frequency space around candidate detections in radio technosignature detection pipelines. These problems include inaccurate estimates of figures of merit and unreliable upper limits on the prevalence of technosignatures. We present an algorithm that mitigates these problems and improves the efficiency of the search. Specifically, our new algorithm increases the number of candidate detections by a factor of more than four compared to the results of Margot et al.