https://www.nature.com/articles/s41550-022-01872-z Skip to main content Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript. Advertisement Advertisement Nature Astronomy * View all journals * Search * Log in * Explore content * About the journal * Publish with us * Subscribe * Sign up for alerts * RSS feed 1. nature 2. nature astronomy 3. articles 4. article * Article * Published: 30 January 2023 A deep-learning search for technosignatures from 820 nearby stars * Peter Xiangyuan Ma ORCID: orcid.org/0000-0001-8975-3719^1,2,3, * Cherry Ng ORCID: orcid.org/0000-0002-3616-5160^3,4,5, * Leandro Rizk^3, * Steve Croft ORCID: orcid.org/0000-0003-4823-129X^4,5, * Andrew P. V. Siemion^4,5,6,7, * Bryan Brzycki^4, * Daniel Czech^4, * Jamie Drew^8, * Vishal Gajjar ORCID: orcid.org/0000-0002-8604-106X^4, * John Hoang^4, * Howard Isaacson ORCID: orcid.org/0000-0002-0531-1073^4,9, * Matt Lebofsky ORCID: orcid.org/0000-0002-7042-7566^4, * David H. E. MacMahon ORCID: orcid.org/0000-0001-6950-5072^4, * Imke de Pater ORCID: orcid.org/0000-0002-4278-3168^4,10, * Danny C. Price ORCID: orcid.org/0000-0003-2783-1608^4,11, * Sofia Z. Sheikh ORCID: orcid.org/0000-0001-7057-4999^4 & * ... * S. Pete Worden^8 Show authors Nature Astronomy (2023)Cite this article * 2112 Accesses * 1 Citations * 1372 Altmetric * Metrics details Subjects * Computational science * Scientific data * Transient astrophysical phenomena 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 b- 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. Access through your institution Buy or subscribe This is a preview of subscription content, access via your institution Access options Access through your institution Access through your institution Change institution Buy or subscribe Access Nature and 54 other Nature Portfolio journals Get Nature+, our best-value online-access subscription $29.99 per month cancel any time Learn more Subscribe to this journal Receive 12 digital issues and online access to articles $119.00 per year only $9.92 per issue Learn more Rent or buy this article Get just this article for as long as you need it $39.95 Learn more Prices may be subject to local taxes which are calculated during checkout Additional access options: * Log in * Learn about institutional subscriptions Fig. 1: The distribution of signals of interest in terms of confidence threshold and observing frequency. [41550_2022_1872_Fig1_HTML] Fig. 2: Waterfall plots of the eight signals of interest. [41550_2022_1872_Fig2_HTML] Fig. 3: Model training and execution scheme. [41550_2022_1872_Fig3_HTML] Fig. 4: Examples showing the four types of training data. [41550_2022_1872_Fig4_HTML] Fig. 5: A ROC comparing the true-positive rate against the false-positive rate at various threshold settings for a number of ML models. [41550_2022_1872_Fig5_HTML] 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. References 1. Cocconi, G. & Morrison, P. Searching for interstellar communications. Nature 184, 844-846 (1959). Article ADS Google Scholar 2. Tarter, J. The search for extraterrestrial intelligence (SETI). Annu. Rev. Astron. Astrophys. 39, 511-548 (2001). Article ADS Google Scholar 3. Enriquez, J. E. et al. The Breakthrough Listen search for intelligent life: 1.1-1.9 GHz observations of 692 nearby stars. Astrophys. J. 849, 104 (2017). Article ADS Google Scholar 4. Price, D. C. et al. The Breakthrough Listen search for intelligent life: wide-bandwidth digital instrumentation for the CSIRO Parkes 64-m telescope. Publ. Astron. Soc. Aust. 35, E041 (2018). 5. Price, D. C. et al. The Breakthrough Listen search for intelligent life: observations of 1327 nearby stars over 1.10-3.45 GHz. Astron. J. 159, 86 (2020). Article ADS Google Scholar 6. Price, D. C. et al. Expanded capability of the Breakthrough Listen Parkes data recorder for observations with the UWL receiver. Res. Notes AAS 5, 114 (2021). Article ADS Google Scholar 7. Enriquez, E. & Price, D. turboSETI: Python-based SETI search algorithm. Astrophysics Source Code Library ascl:1906.006 (2019). 8. Harp, G. R. et al. Machine vision and deep learning for classification of radio SETI signals. Preprint at https:// arxiv.org/abs/1902.02426 (2019). 9. Zhang, Z.-S. et al. First SETI observations with China's Five-hundred-meter Aperture Spherical Radio Telescope (FAST). Astrophys. J. 891, 174 (2020). Article ADS Google Scholar 10. Pinchuk, P. & Margot, J.-L. A machine learning-based direction-of-origin filter for the identification of radio frequency interference in the search for technosignatures. Astron. J. 163, 76 (2022). 11. Czech, D., Mishra, A. & Inggs, M. A CNN and LSTM-based approach to classifying transient radio frequency interference. Astron. Comput. 25, 52-57 (2018). Article ADS Google Scholar 12. Zhang, Y. G., Hyun Won, K., Son, S. W., Siemion, A. & Croft, S. Self-supervised anomaly detection for narrowband seti. In 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP) 1114-1118 (IEEE, 2018). 13. Brzycki, B. et al. Narrow-band signal localization for SETI on noisy synthetic spectrogram data. Publ. Astron. Soc. Pac. 132, 114501 (2020). Article ADS Google Scholar 14. Higgins, I. et al. b-VAE: Learning basic visual concepts with a constrained variational framework. In International Conference on Learning Representations (ICLR, 2017). 15. Kingma, D. P. & Welling, M. Auto-encoding variational Bayes. In 2nd International Conference on Learning Representations (ICLR, 2014); http://arxiv.org/abs/1312.6114v10.8/25 16. Czech, D. et al. The Breakthrough Listen search for Intelligent Life: MeerKAT target selection. Publ. Astron. Soc. Pac. 133, 064502 (2021). Article ADS Google Scholar 17. Hickish, J. et al. Commensal, multi-user observations with an ethernet-based Jansky Very Large Array. Bul. Am. Astron. Soc. 51, 269 (2019). 18. Siemion, A. et al. Searching for extraterrestrial intelligence with the square kilometre array. In Proceedings of Advancing Astrophysics with the Square Kilometre Array--PoS(AASKA14) (Sissa Medialab, 2015). 19. LeCun, Y., Haffner, P., Bottou, L. & Bengio, Y. in Shape, Contour and Grouping in Computer Vision: Lecture Notes in Computer Science (eds Forsyth, D. A. et al.) 319-345 (Springer, 1999); https://doi.org/10.1007/3-540-46805-6_19 20. Snoek, J., Larochelle, H. & Adams, R. P. Practical Bayesian optimization of machine learning algorithms. In Advances in Neural Information Processing Systems 25 (NIPS, 2012). 21. Abadi, M. et al. TensorFlow: large-scale machine learning on heterogeneous systems (TensorFlow, 2015); https:// www.tensorflow.org/ 22. Chollet, F. et al. Keras (Keras, 2015); https://keras.io 23. Mitchell, T. M. The Need for Biases in Learning Generalizations. Tech. Rep., Rutgers University (1980). 24. Breiman, L. Random forests. Machine Learning 45, 5-32 (2001). Article MATH Google Scholar 25. Virtanen, P. et al. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat. Methods 17, 261-272 (2020). Article Google Scholar 26. LeCun, Y. et al. Backpropagation applied to handwritten zip code recognition. Neural Comput. 1, 541-551 (1989). Article Google Scholar 27. Cristianini, N. & Ricci, E. in Encyclopedia of Algorithms (ed, Kao, M.-Y.) 928-932 (Springer, 2008); https://doi.org/10.1007/ 978-0-387-30162-4_415 28. Lebofsky, M. et al. The Breakthrough Listen search for intelligent life: public data, formats, reduction, and archiving. Publ. Astron. Soc. Pac. 131, 124505 (2019). Article ADS Google Scholar 29. Price, D., Enriquez, J., Chen, Y. & Siebert, M. Blimpy: Breakthrough Listen I/O methods for Python. J. Open Source Softw. 4, 1554 (2019). Article ADS Google Scholar 30. Lam, S. K., Pitrou, A. & Seibert, S. Numba: A LLVM-based Python JIT compiler. In Proc. Second Workshop on the LLVM Compiler Infrastructure in HPC, LLVM '15 1-6 (Association for Computing Machinery, 2015); https://doi.org/10.1145/2833157.2833162 31. Sochat, V. Singularity compose: orchestration for singularity instances. J. Open Source Softw. 4, 1578 (2019). Article ADS Google Scholar 32. Siemion, A. P. V. et al. A 1.1-1.9 GHz SETI survey of the Kepler field. I. A search for narrow-band emission from select targets. Astrophys. J. 767, 94 (2013). Article ADS Google Scholar 33. Perryman, M. A. C. et al. The Hipparcos Catalogue. Astron. Astrophys. 500, 501-504 (1997). ADS Google Scholar Download references 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. Author information 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 Authors 1. Peter Xiangyuan Ma View author publications You can also search for this author in PubMed Google Scholar 2. Cherry Ng View author publications You can also search for this author in PubMed Google Scholar 3. Leandro Rizk View author publications You can also search for this author in PubMed Google Scholar 4. Steve Croft View author publications You can also search for this author in PubMed Google Scholar 5. Andrew P. V. Siemion View author publications You can also search for this author in PubMed Google Scholar 6. Bryan Brzycki View author publications You can also search for this author in PubMed Google Scholar 7. Daniel Czech View author publications You can also search for this author in PubMed Google Scholar 8. Jamie Drew View author publications You can also search for this author in PubMed Google Scholar 9. Vishal Gajjar View author publications You can also search for this author in PubMed Google Scholar 10. John Hoang View author publications You can also search for this author in PubMed Google Scholar 11. Howard Isaacson View author publications You can also search for this author in PubMed Google Scholar 12. Matt Lebofsky View author publications You can also search for this author in PubMed Google Scholar 13. David H. E. MacMahon View author publications You can also search for this author in PubMed Google Scholar 14. Imke de Pater View author publications You can also search for this author in PubMed Google Scholar 15. Danny C. Price View author publications You can also search for this author in PubMed Google Scholar 16. Sofia Z. Sheikh View author publications You can also search for this author in PubMed Google Scholar 17. S. Pete Worden View author publications You can also search for this author in PubMed Google Scholar 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. Ethics declarations Competing interests The authors declare no competing interests. Peer review Peer review information Nature Astronomy thanks Devansh Agarwal, Tong-Jie Zhang and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Additional information Publisher's note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Supplementary information Supplementary Information Supplementary Tables 1-6, Figs. 1-12 and Discussion. Rights and permissions Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Reprints and Permissions About this article Verify currency and authenticity via CrossMark Cite this article 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 Download citation * Received: 10 December 2021 * Accepted: 30 November 2022 * Published: 30 January 2023 * DOI: https://doi.org/10.1038/s41550-022-01872-z Share this article Anyone you share the following link with will be able to read this content: Get shareable link Sorry, a shareable link is not currently available for this article. Copy to clipboard Provided by the Springer Nature SharedIt content-sharing initiative This article is cited by * Will an AI be the first to discover alien life? + Alexandra Witze Nature (2023) Access through your institution Buy or subscribe Access through your institution Change institution Buy or subscribe Advertisement Advertisement Explore content * Research articles * Reviews & Analysis * News & Comment * Videos * Current issue * Collections * Follow us on Twitter * Subscribe * Sign up for alerts * RSS feed About the journal * Aims & Scope * Journal Information * Journal Metrics * About the Editors * Our publishing models * Editorial Values Statement * Editorial Policies * Content Types * Contact Publish with us * Submission Guidelines * For Reviewers * Language editing services * Submit manuscript Search Search articles by subject, keyword or author [ ] Show results from [All journals] Search Advanced search Quick links * Explore articles by subject * Find a job * Guide to authors * Editorial policies Nature Astronomy (Nat Astron) ISSN 2397-3366 (online) nature.com sitemap About Nature Portfolio * About us * Press releases * Press office * Contact us Discover content * Journals A-Z * Articles by subject * Nano * Protocol Exchange * Nature Index Publishing policies * Nature portfolio policies * Open access Author & Researcher services * Reprints & permissions * Research data * Language editing * Scientific editing * Nature Masterclasses * Nature Research Academies * Research Solutions Libraries & institutions * Librarian service & tools * Librarian portal * Open research * Recommend to library Advertising & partnerships * Advertising * Partnerships & Services * Media kits * Branded content Career development * Nature Careers * Nature Conferences * Nature events Regional websites * Nature Africa * Nature China * Nature India * Nature Italy * Nature Japan * Nature Korea * Nature Middle East * Privacy Policy * Use of cookies * Manage cookies/Do not sell my data * Legal notice * Accessibility statement * Terms & Conditions * California Privacy Statement Springer Nature (c) 2023 Springer Nature Limited Close Nature Briefing Sign up for the Nature Briefing newsletter -- what matters in science, free to your inbox daily. Email address [ ] Sign up [ ] I agree my information will be processed in accordance with the Nature and Springer Nature Limited Privacy Policy. Close Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing * *