Using Anomaly Detection to Search for Technosignatures in Breakthrough Listen Observations
We implement a machine learning algorithm to search for extraterrestrial technosignatures in radio observations of several hundred nearby stars, obtained with the Parkes and Green Bank Telescopes by the Breakthrough Listen collaboration. Advances in detection technology have led to an exponential gr...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IOP Publishing
2025-01-01
|
| Series: | The Astronomical Journal |
| Subjects: | |
| Online Access: | https://doi.org/10.3847/1538-3881/add52b |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849467993638043648 |
|---|---|
| author | Snir Pardo Dovi Poznanski Steve Croft Andrew P. V. Siemion Matthew Lebofsky |
| author_facet | Snir Pardo Dovi Poznanski Steve Croft Andrew P. V. Siemion Matthew Lebofsky |
| author_sort | Snir Pardo |
| collection | DOAJ |
| description | We implement a machine learning algorithm to search for extraterrestrial technosignatures in radio observations of several hundred nearby stars, obtained with the Parkes and Green Bank Telescopes by the Breakthrough Listen collaboration. Advances in detection technology have led to an exponential growth in data, necessitating innovative and efficient analysis methods. This problem is exacerbated by the large variety of possible forms an extraterrestrial signal might take, and the size of the multidimensional parameter space that must be searched. It is then made markedly worse by the fact that our best guess at the properties of such a signal is that it might resemble the signals emitted by human technology and communications, the main (yet diverse) contaminant in radio observations. We address this challenge by using a combination of simulations and machine learning methods for anomaly detection. We rank candidates by how unusual they are in frequency, and how persistent they are in time, by measuring the similarity between consecutive spectrograms of the same star. We validate that our filters significantly improve the quality of the candidates that are selected for human vetting when compared to a random selection. Of the ∼10 ^11 spectrograms that we analyzed, we visually inspected thousands of the most promising spectrograms, and thousands more for validation, about 20,000 in total, and report that no candidate survived basic scrutiny. |
| format | Article |
| id | doaj-art-c4bddfcb36d84e64a66a7f614b289a7a |
| institution | Kabale University |
| issn | 1538-3881 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | The Astronomical Journal |
| spelling | doaj-art-c4bddfcb36d84e64a66a7f614b289a7a2025-08-20T03:25:59ZengIOP PublishingThe Astronomical Journal1538-38812025-01-0117011210.3847/1538-3881/add52bUsing Anomaly Detection to Search for Technosignatures in Breakthrough Listen ObservationsSnir Pardo0Dovi Poznanski1https://orcid.org/0000-0003-1470-7173Steve Croft2https://orcid.org/0000-0003-4823-129XAndrew P. V. Siemion3https://orcid.org/0000-0003-2828-7720Matthew Lebofsky4https://orcid.org/0000-0002-7042-7566School of Physics and Astronomy, Tel-Aviv University , Tel-Aviv 69978, Israel ; snir.pardo92@gmail.com, dovi@tau.ac.ilSchool of Physics and Astronomy, Tel-Aviv University , Tel-Aviv 69978, Israel ; snir.pardo92@gmail.com, dovi@tau.ac.il; Cahill Center for Astrophysics, California Institute of Technology , Pasadena, CA 91125, USA; Kavli Institute for Particle Astrophysics & Cosmology, 452 Lomita Mall, Stanford University , Stanford, CA 94305, USA; Department of Physics, Stanford University , 382 Via Pueblo Mall, Stanford, CA 94305, USASETI Institute , 339 Bernardo Ave, Suite 200, Mountain View, CA 94043, USA; Breakthrough Listen, University of California , Berkeley, 3501 Campbell Hall 3411, Berkeley, CA 94720, USA; Department of Physics, University of Oxford , Denys Wilkinson Building, Keble Road, Oxford, OX1 3RH, UKSETI Institute , 339 Bernardo Ave, Suite 200, Mountain View, CA 94043, USA; Breakthrough Listen, University of California , Berkeley, 3501 Campbell Hall 3411, Berkeley, CA 94720, USA; Department of Physics, University of Oxford , Denys Wilkinson Building, Keble Road, Oxford, OX1 3RH, UKBreakthrough Listen, University of California , Berkeley, 3501 Campbell Hall 3411, Berkeley, CA 94720, USAWe implement a machine learning algorithm to search for extraterrestrial technosignatures in radio observations of several hundred nearby stars, obtained with the Parkes and Green Bank Telescopes by the Breakthrough Listen collaboration. Advances in detection technology have led to an exponential growth in data, necessitating innovative and efficient analysis methods. This problem is exacerbated by the large variety of possible forms an extraterrestrial signal might take, and the size of the multidimensional parameter space that must be searched. It is then made markedly worse by the fact that our best guess at the properties of such a signal is that it might resemble the signals emitted by human technology and communications, the main (yet diverse) contaminant in radio observations. We address this challenge by using a combination of simulations and machine learning methods for anomaly detection. We rank candidates by how unusual they are in frequency, and how persistent they are in time, by measuring the similarity between consecutive spectrograms of the same star. We validate that our filters significantly improve the quality of the candidates that are selected for human vetting when compared to a random selection. Of the ∼10 ^11 spectrograms that we analyzed, we visually inspected thousands of the most promising spectrograms, and thousands more for validation, about 20,000 in total, and report that no candidate survived basic scrutiny.https://doi.org/10.3847/1538-3881/add52bSearch for extraterrestrial intelligence |
| spellingShingle | Snir Pardo Dovi Poznanski Steve Croft Andrew P. V. Siemion Matthew Lebofsky Using Anomaly Detection to Search for Technosignatures in Breakthrough Listen Observations The Astronomical Journal Search for extraterrestrial intelligence |
| title | Using Anomaly Detection to Search for Technosignatures in Breakthrough Listen Observations |
| title_full | Using Anomaly Detection to Search for Technosignatures in Breakthrough Listen Observations |
| title_fullStr | Using Anomaly Detection to Search for Technosignatures in Breakthrough Listen Observations |
| title_full_unstemmed | Using Anomaly Detection to Search for Technosignatures in Breakthrough Listen Observations |
| title_short | Using Anomaly Detection to Search for Technosignatures in Breakthrough Listen Observations |
| title_sort | using anomaly detection to search for technosignatures in breakthrough listen observations |
| topic | Search for extraterrestrial intelligence |
| url | https://doi.org/10.3847/1538-3881/add52b |
| work_keys_str_mv | AT snirpardo usinganomalydetectiontosearchfortechnosignaturesinbreakthroughlistenobservations AT dovipoznanski usinganomalydetectiontosearchfortechnosignaturesinbreakthroughlistenobservations AT stevecroft usinganomalydetectiontosearchfortechnosignaturesinbreakthroughlistenobservations AT andrewpvsiemion usinganomalydetectiontosearchfortechnosignaturesinbreakthroughlistenobservations AT matthewlebofsky usinganomalydetectiontosearchfortechnosignaturesinbreakthroughlistenobservations |