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

Anne D. Brooks, Robert A. Lodder

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


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 languageEnglish
Title of host publicationComputational Science – ICCS 2019 - 19th International Conference, Proceedings
EditorsJoão M.F. Rodrigues, Pedro J.S. Cardoso, Jânio Monteiro, Roberto Lam, Valeria V. Krzhizhanovskaya, Michael H. Lees, Peter M.A. Sloot, Jack J. Dongarra
Number of pages13
StatePublished - 2019
Event19th International Conference on Computational Science, ICCS 2019 - Faro, Portugal
Duration: Jun 12 2019Jun 14 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11539 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference19th International Conference on Computational Science, ICCS 2019

Bibliographical note

Publisher Copyright:
© 2019, Springer Nature Switzerland AG.


  • Bootstrap
  • Gamma ray burst
  • Parallel algorithm
  • Solar transit
  • Supernova

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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