Cardiff University | Prifysgol Caerdydd ORCA
Online Research @ Cardiff 
WelshClear Cookie - decide language by browser settings

Coherent Network Analysis Technique for Discriminating Gravitational-Wave Bursts from Instrumental Noise

Chatterji, Shourov, Lazzarini, Albert, Stein, Leo, Sutton, Patrick J, Searle, Antony and Tinto, Massimo 2006. Coherent Network Analysis Technique for Discriminating Gravitational-Wave Bursts from Instrumental Noise. Physical Review -Series D- 74 , 082005. 10.1103/PhysRevD.74.082005

[img]
Preview
PDF
Download (3MB) | Preview

Abstract

The sensitivity of current searches for gravitational-wave bursts is limited by non-Gaussian, nonstationary noise transients which are common in real detectors. Existing techniques for detecting gravitational-wave bursts assume the output of the detector network to be the sum of a stationary Gaussian noise process and a gravitational-wave signal. These techniques often fail in the presence of noise nonstationarities by incorrectly identifying such transients as possible gravitational-wave bursts. Furthermore, consistency tests currently used to try to eliminate these noise transients are not applicable to general networks of detectors with different orientations and noise spectra. In order to address this problem we introduce a fully coherent consistency test that is robust against noise nonstationarities and allows one to distinguish between gravitational-wave bursts and noise transients in general detector networks. This technique does not require any a priori knowledge of the putative burst waveform.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Physics and Astronomy
Subjects: Q Science > QC Physics
Publisher: American Physical Society
ISSN: 0556-2821
Last Modified: 18 Oct 2018 01:30
URI: http://orca.cf.ac.uk/id/eprint/1587

Citation Data

Cited 79 times in Google Scholar. View in Google Scholar

Cited 72 times in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item Edit Item

Downloads

Downloads per month over past year

View more statistics