Separating Gravitational Wave Signals from Instrument Artifacts
arXiv:1008.1577 · doi:10.1103/PhysRevD.82.103007
Abstract
Central to the gravitational wave detection problem is the challenge of separating features in the data produced by astrophysical sources from features produced by the detector. Matched filtering provides an optimal solution for Gaussian noise, but in practice, transient noise excursions or ``glitches'' complicate the analysis. Detector diagnostics and coincidence tests can be used to veto many glitches which may otherwise be misinterpreted as gravitational wave signals. The glitches that remain can lead to long tails in the matched filter search statistics and drive up the detection threshold. Here we describe a Bayesian approach that incorporates a more realistic model for the instrument noise allowing for fluctuating noise levels that vary independently across frequency bands, and deterministic ``glitch fitting'' using wavelets as ``glitch templates'', the number of which is determined by a trans-dimensional Markov chain Monte Carlo algorithm. We demonstrate the method's effectiveness on simulated data containing low amplitude gravitational wave signals from inspiraling binary black hole systems, and simulated non-stationary and non-Gaussian noise comprised of a Gaussian component with the standard LIGO/Virgo spectrum, and injected glitches of various amplitude, prevalence, and variety. Glitch fitting allows us to detect significantly weaker signals than standard techniques.
21 pages, 18 figures
References in corpus (22)
- Coherent method for detection of gravitational wave bursts
- Toward faithful templates for non-spinning binary black holes using the effective-one-body approach
- LISA detections of massive black hole inspirals: parameter extraction errors due to inaccurate template waveforms
- Effective-one-body waveforms calibrated to numerical relativity simulations: coalescence of non-precessing, spinning, equal-mass black holes
- Search for Gravitational Waves from Low Mass Binary Coalescences in the First Year of LIGO's S5 Data
- Tests of Bayesian Model Selection Techniques for Gravitational Wave Astronomy
- Gravitational-Wave Astronomy with Inspiral Signals of Spinning Compact-Object Binaries
- Parameter estimation of spinning binary inspirals using Markov-chain Monte Carlo
- A Solution to the Galactic Foreground Problem for LISA
- A Bayesian Approach to the Detection Problem in Gravitational Wave Astronomy
- Localizing coalescing massive black hole binaries with gravitational waves
- The Effect of Higher Harmonic Corrections on the Detection of massive black hole binaries with LISA
- A Bayesian approach to the follow-up of candidate gravitational wave signals
- An Evidence Based Search Method For Gravitational Waves From Neutron Star Ring-downs
- Parameter estimation for signals from compact binary inspirals injected into LIGO data
- A Constrained Metropolis-Hastings Search for EMRIs in the Mock LISA Data Challenge 1B
- A Three-Stage Search for Supermassive Black Hole Binaries in LISA Data
- Searching for Massive Black Hole Binaries in the first Mock LISA Data Challenge
- Physical instrumental vetoes for gravitational-wave burst triggers
- Modeling the Impulsive Noise Component and its Effect on the Operation of a Simple Coherent Network Algorithm for Unmodeled Gravitational Wave Bursts Detection
- Search for a stochastic gravitational-wave signal in the second round of the Mock LISA Data Challenges
- Markov chain Monte Carlo searches for Galactic binaries in Mock LISA Data Challenge 1B data sets