Nonparametric Approach to Weak Signal Detection in the Search for Extraterrestrial Intelligence (SETI)

Anne D. Brooks; Robert A. Lodder

doi:10.1007/978-3-030-22747-0_1

Nonparametric Approach to Weak Signal Detection in the Search for Extraterrestrial Intelligence (SETI)

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

It might be easier for intelligent extraterrestrial civilizations to be found when they mark their position with a bright laser beacon. Given the possible distances involved, however, it is likely that weak signal detection techniques would still be required to identify even the brightest SETI Beacon. The Bootstrap Error-adjusted Single-sample Technique (BEST) is such a detection method. The BEST has been shown to outperform the more traditional Mahalanobis metric in analysis of SETI data from a Project Argus near infrared telescope. The BEST algorithm is used to identify unusual signals and returns a distance in asymmetric nonparametric multidimensional central 68% confidence intervals (equivalent to standard deviations for 1-D data that are normally distributed, or Mahalanobis distance units for normally distributed data of d dimensions). Calculation of the Mahalanobis metric requires matrix factorization and is order of d³. Furthermore, the accuracy and precision of the BEST metric are greater than the Mahalanobis metric in realistic data collection scenarios (many more wavelengths available then observations at those wavelengths). An extension of the BEST to examine multiple samples (subclusters of data) simultaneously is explored in this paper.

Original language	English
Title of host publication	Computational Science – ICCS 2019 - 19th International Conference, Proceedings
Editors	João M.F. Rodrigues, Pedro J.S. Cardoso, Jânio Monteiro, Roberto Lam, Valeria V. Krzhizhanovskaya, Michael H. Lees, Peter M.A. Sloot, Jack J. Dongarra
Pages	3-15
Number of pages	13
DOIs	https://doi.org/10.1007/978-3-030-22747-0_1
State	Published - 2019
Event	19th International Conference on Computational Science, ICCS 2019 - Faro, Portugal Duration: Jun 12 2019 → Jun 14 2019

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11539 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	19th International Conference on Computational Science, ICCS 2019
Country/Territory	Portugal
City	Faro
Period	6/12/19 → 6/14/19

Bibliographical note

Publisher Copyright:
© 2019, Springer Nature Switzerland AG.

Keywords

Bootstrap
Gamma ray burst
Parallel algorithm
Solar transit
Supernova

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-030-22747-0_1

Cite this

Brooks, A. D., & Lodder, R. A. (2019). Nonparametric Approach to Weak Signal Detection in the Search for Extraterrestrial Intelligence (SETI). In J. M. F. Rodrigues, P. J. S. Cardoso, J. Monteiro, R. Lam, V. V. Krzhizhanovskaya, M. H. Lees, P. M. A. Sloot, & J. J. Dongarra (Eds.), Computational Science – ICCS 2019 - 19th International Conference, Proceedings (pp. 3-15). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11539 LNCS). https://doi.org/10.1007/978-3-030-22747-0_1

Brooks, Anne D. ; Lodder, Robert A. / Nonparametric Approach to Weak Signal Detection in the Search for Extraterrestrial Intelligence (SETI). Computational Science – ICCS 2019 - 19th International Conference, Proceedings. editor / João M.F. Rodrigues ; Pedro J.S. Cardoso ; Jânio Monteiro ; Roberto Lam ; Valeria V. Krzhizhanovskaya ; Michael H. Lees ; Peter M.A. Sloot ; Jack J. Dongarra. 2019. pp. 3-15 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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Brooks, AD & Lodder, RA 2019, Nonparametric Approach to Weak Signal Detection in the Search for Extraterrestrial Intelligence (SETI). in JMF Rodrigues, PJS Cardoso, J Monteiro, R Lam, VV Krzhizhanovskaya, MH Lees, PMA Sloot & JJ Dongarra (eds), Computational Science – ICCS 2019 - 19th International Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11539 LNCS, pp. 3-15, 19th International Conference on Computational Science, ICCS 2019, Faro, Portugal, 6/12/19. https://doi.org/10.1007/978-3-030-22747-0_1

Nonparametric Approach to Weak Signal Detection in the Search for Extraterrestrial Intelligence (SETI). / Brooks, Anne D.; Lodder, Robert A.
Computational Science – ICCS 2019 - 19th International Conference, Proceedings. ed. / João M.F. Rodrigues; Pedro J.S. Cardoso; Jânio Monteiro; Roberto Lam; Valeria V. Krzhizhanovskaya; Michael H. Lees; Peter M.A. Sloot; Jack J. Dongarra. 2019. p. 3-15 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11539 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - Nonparametric Approach to Weak Signal Detection in the Search for Extraterrestrial Intelligence (SETI)

AU - Brooks, Anne D.

AU - Lodder, Robert A.

PY - 2019

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N2 - It might be easier for intelligent extraterrestrial civilizations to be found when they mark their position with a bright laser beacon. Given the possible distances involved, however, it is likely that weak signal detection techniques would still be required to identify even the brightest SETI Beacon. The Bootstrap Error-adjusted Single-sample Technique (BEST) is such a detection method. The BEST has been shown to outperform the more traditional Mahalanobis metric in analysis of SETI data from a Project Argus near infrared telescope. The BEST algorithm is used to identify unusual signals and returns a distance in asymmetric nonparametric multidimensional central 68% confidence intervals (equivalent to standard deviations for 1-D data that are normally distributed, or Mahalanobis distance units for normally distributed data of d dimensions). Calculation of the Mahalanobis metric requires matrix factorization and is order of d3. Furthermore, the accuracy and precision of the BEST metric are greater than the Mahalanobis metric in realistic data collection scenarios (many more wavelengths available then observations at those wavelengths). An extension of the BEST to examine multiple samples (subclusters of data) simultaneously is explored in this paper.

AB - It might be easier for intelligent extraterrestrial civilizations to be found when they mark their position with a bright laser beacon. Given the possible distances involved, however, it is likely that weak signal detection techniques would still be required to identify even the brightest SETI Beacon. The Bootstrap Error-adjusted Single-sample Technique (BEST) is such a detection method. The BEST has been shown to outperform the more traditional Mahalanobis metric in analysis of SETI data from a Project Argus near infrared telescope. The BEST algorithm is used to identify unusual signals and returns a distance in asymmetric nonparametric multidimensional central 68% confidence intervals (equivalent to standard deviations for 1-D data that are normally distributed, or Mahalanobis distance units for normally distributed data of d dimensions). Calculation of the Mahalanobis metric requires matrix factorization and is order of d3. Furthermore, the accuracy and precision of the BEST metric are greater than the Mahalanobis metric in realistic data collection scenarios (many more wavelengths available then observations at those wavelengths). An extension of the BEST to examine multiple samples (subclusters of data) simultaneously is explored in this paper.

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KW - Solar transit

KW - Supernova

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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Brooks AD, Lodder RA. Nonparametric Approach to Weak Signal Detection in the Search for Extraterrestrial Intelligence (SETI). In Rodrigues JMF, Cardoso PJS, Monteiro J, Lam R, Krzhizhanovskaya VV, Lees MH, Sloot PMA, Dongarra JJ, editors, Computational Science – ICCS 2019 - 19th International Conference, Proceedings. 2019. p. 3-15. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-22747-0_1

Nonparametric Approach to Weak Signal Detection in the Search for Extraterrestrial Intelligence (SETI)

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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