Toward the detection of gravitational waves under non-Gaussian noises I. Locally optimal statistic

Abstract

After reviewing the standard hypothesis test and the matched filter technique to identify gravitational waves under Gaussian noises, we introduce two methods to deal with non-Gaussian stationary noises. We formulate the likelihood ratio function under weakly non-Gaussian noises through the Edgeworth expansion and strongly non-Gaussian noises in terms of a new method we call Gaussian mapping where the observed marginal distribution and the two-body correlation function are fully taken into account. We then apply these two approaches to Student’s t-distribution which has a larger tails than Gaussian. It is shown that while both methods work well in the case the non-Gaussianity is small, only the latter method works well for highly non-Gaussian case.

Journal

Journal ID (nlm-ta): Proc Jpn Acad Ser B Phys Biol Sci

Journal ID (iso-abbrev): Proc. Jpn. Acad., Ser. B, Phys. Biol. Sci

Journal ID (publisher-id): PJAB

Title: Proceedings of the Japan Academy. Series B, Physical and Biological Sciences

Publisher: The Japan Academy (Tokyo, Japan )

ISSN (Print): 0386-2208

ISSN (Electronic): 1349-2896

Publication date (Print): 11 December 2014

Volume: 90

Issue: 10

Pages: 422-432

Affiliations

[*1 ]Research Center for the Early Universe (RESCEU), Graduate School of Science, The University of Tokyo, Tokyo, Japan.

[*2 ]Kavli Institute for the Physics and Mathematics of the Universe (Kavli IPMU), TODIAS, WPI, The University of Tokyo, Kashiwa, Chiba, Japan.

Author notes

[† ]Correspondence should be addressed: J. Yokoyama, Research Center for the Early Universe (RESCEU), Graduate School of Science, The University of Tokyo, 113-0033, Japan (e-mail: yokoyama@ 123456resceu.s.u-tokyo.ac.jp ).

(Communicated by Yoshihide KOZAI, M.J.A.)

Article

Publisher ID: pjab-90-422

DOI: 10.2183/pjab.90.422

PMC ID: 4335139

PubMed ID: 25504231

Copyright statement: © 2014 The Japan Academy

License:

This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Categories

Subject: Original Article