Data availability
All data used in this paper are stored as high-resolution FILTERBANK and HDF5 format collected and generated from observations by the Robert C. Byrd Green Bank Telescope, which are available through the Breakthrough Listen Open Data Archive at http://seti.berkeley.edu/opendata.
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Acknowledgements
Breakthrough Listen is managed by the Breakthrough Initiatives, sponsored by the Breakthrough Prize Foundation (http://www.breakthroughinitiatives.org). We are grateful to the staff of the Green Bank Observatory for their help with installation and commissioning of the Breakthrough Listen backend instrument and extensive support during Breakthrough Listen observations. P.X.M. was supported by the Laidlaw foundation, which has funded this project as part of the undergraduate research and leadership funding initiative. S.Z.S. acknowledges that this material is based on work supported by the National Science Foundation MPS-Ascend Postdoctoral Research Fellowship under grant number 2138147. We thank Y. Chen for helpful discussion on the machine-learning framework. P.X.M. thanks L. Doyle and S. Marzen for their kind support, generous guidance and encouragement when he first began his research career.
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Ma, P.X., Ng, C., Rizk, L. et al. A deep-learning search for technosignatures from 820 nearby stars.
Nat Astron (2023). https://doi.org/10.1038/s41550-022-01872-z
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DOI: https://doi.org/10.1038/s41550-022-01872-z
