Extraction of black hole coalescence waveforms from noisy data
Creators
- 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.2018.08.009Additional 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.