Published September 2018 | Version v1
Journal article

Extraction of black hole coalescence waveforms from noisy data

  • 1. Perimeter Institute for Theoretical Physics, Waterloo ON N2L 2Y5 (Canada)
  • 2. Department of Physics and Astronomy, University of Waterloo, Waterloo ON N2L 3G1 (Canada)

Description

We describe an independent analysis of LIGO data for black hole coalescence events. Gravitational wave strain waveforms are extracted directly from the data using a filtering method that exploits the observed or expected time-dependent frequency content. Statistical analysis of residual noise, after filtering out spectral peaks (and considering finite bandwidth), shows no evidence of non-Gaussian behaviour. There is also no evidence of anomalous causal correlation between noise signals at the Hanford and Livingston sites. The extracted waveforms are consistent with black hole coalescence template waveforms provided by LIGO. Simulated events, with known signals injected into real noise, are used to determine uncertainties due to residual noise and demonstrate that our results are unbiased. Conceptual and numerical differences between our RMS signal-to-noise ratios (SNRs) and the published matched-filter detection SNRs are discussed.

Availability note (English)

Available from http://dx.doi.org/10.1016/j.physletb.2018.08.009

Additional details

Identifiers

DOI
10.1016/j.physletb.2018.08.009;
arXiv
arXiv:1711.00347v3;
PII
S0370269318306129;

Publishing Information

Journal Title
Physics Letters. Section B
Journal Volume
784
Journal Page Range
p. 312-323
ISSN
0370-2693
CODEN
PYLBAJ

INIS

Country of Publication
Netherlands
Country of Input or Organization
International Atomic Energy Agency (IAEA)
INIS RN
51017470
Subject category
S72: PHYSICS OF ELEMENTARY PARTICLES AND FIELDS;
Descriptors DEI
BLACK HOLES; COALESCENCE; CORRELATIONS; DATA PROCESSING; EXTRACTION; GRAVITATIONAL WAVES; SIGNAL-TO-NOISE RATIO; SIMULATION; TIME DEPENDENCE; WAVE FORMS
Descriptors DEC
DIMENSIONLESS NUMBERS; PROCESSING; SEPARATION PROCESSES

Optional Information

Copyright
Copyright (c) 2017 Elsevier Science B.V., Amsterdam, The Netherlands, All rights reserved.