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A deep-learning search for technosignatures from 820 nearby stars

Nature Astronomy volume 7, pages 492–502 (2023)Cite this article

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Abstract

The goal of the search for extraterrestrial intelligence (SETI) is to quantify the prevalence of technological life beyond Earth via their ‘technosignatures’. One theorized technosignature is narrowband Doppler drifting radio signals. The principal challenge in conducting SETI in the radio domain is developing a generalized technique to reject human radiofrequency interference. Here we present a comprehensive deep-learning-based technosignature search on 820 stellar targets from the Hipparcos catalogue, totalling over 480 h of on-sky data taken with the Robert C. Byrd Green Bank Telescope as part of the Breakthrough Listen initiative. We implement a novel β-convolutional variational autoencoder to identify technosignature candidates in a semi-unsupervised manner while keeping the false-positive rate manageably low, reducing the number of candidate signals by approximately two orders of magnitude compared with previous analyses on the same dataset. Our work also returned eight promising extraterrestrial intelligence signals of interest not previously identified. Re-observations on these targets have so far not resulted in re-detections of signals with similar morphology. This machine-learning approach presents itself as a leading solution in accelerating SETI and other transient research into the age of data-driven astronomy.

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Fig. 1: The distribution of signals of interest in terms of confidence threshold and observing frequency.
Fig. 2: Waterfall plots of the eight signals of interest.
Fig. 3: Model training and execution scheme.
Fig. 4: Examples showing the four types of training data.
Fig. 5: A ROC comparing the true-positive rate against the false-positive rate at various threshold settings for a number of ML models.

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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.

Code availability

The code is available for review at https://github.com/PetchMa/ML_GBT_SETI.

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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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Authors and Affiliations

  1. Department of Mathematics, University of Toronto, Toronto, Ontario, Canada

    Peter Xiangyuan Ma

  2. Department of Physics, University of Toronto, Toronto, Ontario, Canada

    Peter Xiangyuan Ma

  3. Dunlap Institute for Astronomy and Astrophysics, University of Toronto, Toronto, Ontario, Canada

    Peter Xiangyuan Ma, Cherry Ng & Leandro Rizk

  4. Breakthrough Listen, University of California, Berkeley, Berkeley, CA, USA

    Cherry Ng, Steve Croft, Andrew P. V. Siemion, Bryan Brzycki, Daniel Czech, Vishal Gajjar, John Hoang, Howard Isaacson, Matt Lebofsky, David H. E. MacMahon, Imke de Pater, Danny C. Price & Sofia Z. Sheikh

  5. SETI Institute, Mountain View, CA, USA

    Cherry Ng, Steve Croft & Andrew P. V. Siemion

  6. Jodrell Bank Centre for Astrophysics (JBCA), Department of Physics & Astronomy, Alan Turing Building, The University of Manchester, Manchester, UK

    Andrew P. V. Siemion

  7. Institute of Space Sciences and Astronomy, University of Malta, Valletta, Malta

    Andrew P. V. Siemion

  8. Breakthrough Initiatives, Moffett Field, CA, USA

    Jamie Drew & S. Pete Worden

  9. Centre for Astrophysics, University of Southern Queensland, Toowoomba, Queensland, Australia

    Howard Isaacson

  10. Department of Astronomy, University of California, Berkeley, CA, USA

    Imke de Pater

  11. International Centre for Radio Astronomy Research, Curtin University, Bentley, Western Australia, Australia

    Danny C. Price

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Contributions

P.X.M. designed and led the data analysis under the supervision of C.N. with both of them being primary authors of the manuscript. L.R. led the visualization of the candidate diagnostic plots. A.P.V.S., B.B., D.C., V.G., J.H., I.d.P., D.C.P. and S.Z.S. assisted with interpretation, manuscript preparation and revision, and data analysis. S.C. and H.I. helped with the GBT observations and aided manuscript preparation. M.L. and D.H.E.M. provided instrument support, managed data, and aided observations. J.D. and S.P.W. aided manuscript preparation and provided logistical support.

Corresponding author

Correspondence to Peter Xiangyuan Ma.

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The authors declare no competing interests.

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Nature Astronomy thanks Devansh Agarwal, Tong-Jie Zhang and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Supplementary Tables 1–6, Figs. 1–12 and Discussion.

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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 7, 492–502 (2023). https://doi.org/10.1038/s41550-022-01872-z

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