Bayesian coherent analysis of in-spiral gravitational wave signals with a detector network

By J. Veitch, Alberto Vecchio. 2009

167 citations recorded by the source corpus.

The present operation of the ground-based network of gravitational-wave laser interferometers in enhanced configuration and the beginning of the construction of second-generation (or advanced) interferometers with planned observation runs beginning by 2015 bring the search for gravitational waves into a regime where detection is highly plausible. The development of techniques that allow us to discriminate a signal of astrophysical origin from instrumental artefacts in the interferometer data and to extract the full range of information are therefore some of the primary goals of the current work. Here we report the details of a Bayesian approach to the problem of inference for gravitational wave observations using a network (containing an arbitrary number) of instruments, for the computation of the Bayes factor between two hypotheses and the evaluation of the marginalized posterior density functions of the unknown model parameters. The numerical algorithm to tackle the notoriously difficult problem of the evaluation of large multidimensional integrals is based on a technique known as nested sampling, which provides an attractive (and possibly superior) alternative to more traditional Markov-chain Monte Carlo methods. We discuss the details of the implementation of this algorithm and its performance against a Gaussian model of the background noise, considering themore » specific case of the signal produced by the in-spiral of binary systems of black holes and/or neutron stars, although the method is completely general and can be applied to other classes of sources. We also demonstrate the utility of this approach by introducing a new coherence test to distinguish between the presence of a coherent signal of astrophysical origin in the data of multiple instruments and the presence of incoherent accidental artefacts, and the effects on the estimation of the source parameters as a function of the number of instruments in the network.« less

Subfields: Astronomy and Astrophysics, Geophysics

View Paper: https://www.semanticscholar.org/paper/5c55bcda861a6ca572a183dca8022a76e7f13589

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Citations in the corpus

Compact binary coalescences in the band of ground-based gravitational-wave detectors (2009, methodology): “These efforts range from the inclusion of new waveform templates and detection statistics in the pipeline [80] to improved sampling techniques for parameter estimation [88, 92] to simulated runs on numerical-relativity injections as part of the NINJA project [10].”
Binary Black Hole Mergers in the First Advanced LIGO Observing Run (2016, background): “the ratio of the evidence (the marginalized likelihood) for a coherent signal hypothesis to that for Gaussian noise [215].”
Bayesian inference for compact binary coalescences with bilby: validation and application to the first LIGO–Virgo gravitational-wave transient catalogue (2020, background): “Over many years, Markov-chain Monte Carlo (MCMC; Christensen & Meyer 1998, 2001; Röver et al. 2006, 2007; van der Sluys et al. 2008b,a) and nested sampling (Veitch & Vecchio 2008, 2010) algorithms for gravitational-wave inference have been developed.”
Importance Nested Sampling and the MultiNest Algorithm (2013, methodology): “One such implementation (Veitch and Vecchio 2010), with speciﬁc proposal distributions for the MCMC step, has been used successively in gravitational wave searches.”
Bayeswave: Bayesian inference for gravitational wave bursts and instrument glitches (2014, unknown): “Similar ‘glitch hypotheses’ were considered by Veitch and Vecchio [25] in a study of Bayesian model selection applied to the search for black hole inspiral signals.”
STELLAR POPULATIONS IN THE CENTRAL 0.5 pc OF THE GALAXY. II. THE INITIAL MASS FUNCTION (2013, methodology): “This algorithm has been successfully demonstrated in cosmology, galaxy evolution, and gravitational wave problems (e.g. Bridges et al. 2009; Kilbinger et al. 2010; Martinez et al. 2011; Veitch & Vecchio 2010) and is less computationally expensive and more accurate, in some cases, than MCMC methods.”