Astrophysical model selection in gravitational wave astronomy

Adams, M.; Cornish, N.; Littenberg, T. B.

doi:10.1103/physrevd.86.124032

Cited by 60 publications

(46 citation statements)

References 52 publications

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“…Beyond pulsar-timing analysis, this method can be adapted for LIGO or LISA population inference. Current schemes perform demographic analysis to recover the distributions of compact-system properties with either a parametric function [43,44], or using a histogram with bin heights constrained by a GP prior [40,44]. Linking these distributions back to progenitor properties or evolutionary channels has so far only been performed for discrete population synthesis simulations [45].…”

Section: Discussionmentioning

confidence: 99%

Constraints on the Dynamical Environments of Supermassive Black-Hole Binaries Using Pulsar-Timing Arrays

2017

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We introduce a technique for gravitational-wave analysis, where Gaussian process regression is used to emulate the strain spectrum of a stochastic background by training on population-synthesis simulations. This leads to direct Bayesian inference on astrophysical parameters. For PTAs specifically, we interpolate over the parameter space of supermassive black-hole binary environments, including 3-body stellar scattering, and evolving orbital eccentricity. We illustrate our approach on mock data, and assess the prospects for inference with data similar to the NANOGrav 9-yr data release.

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Section: Discussionmentioning

confidence: 99%

Constraints on the Dynamical Environments of Supermassive Black-Hole Binaries Using Pulsar-Timing Arrays

2017

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“…Using Bayesian hierarchical modeling [33,34], we can marginalize over M to obtain a posterior on α. Before sketching how this works, we note that there are three good reasons for eventually developing a sophisticated hyper-parameterization scheme.…”

Section: Population Inferencementioning

confidence: 99%

Optimal Search for an Astrophysical Gravitational-Wave Background

Smith

Thrane²

2018

Phys. Rev. X

110

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Roughly every 2-10 minutes, a pair of stellar mass black holes merge somewhere in the Universe. A small fraction of these mergers are detected as individually resolvable gravitational-wave events by advanced detectors such as LIGO and Virgo. The rest contribute to a stochastic background. We derive the statistically optimal search strategy (producing minimum credible intervals) for a background of unresolved binaries. Our method applies Bayesian parameter estimation to all available data. Using Monte Carlo simulations, we demonstrate that the search is both "safe" and effective: it is not fooled by instrumental artifacts such as glitches and it recovers simulated stochastic signals without bias. Given realistic assumptions, we estimate that the search can detect the binary black hole background with about one day of design sensitivity data versus ≈ 40 months using the traditional cross-correlation search. This framework independently constrains the merger rate and black hole mass distribution, breaking a degeneracy present in the cross-correlation approach. The search provides a unified framework for population studies of compact binaries, which is cast in terms of hyper-parameter estimation. We discuss a number of extensions and generalizations including: application to other sources (such as binary neutron stars and continuous-wave sources), simultaneous estimation of a continuous Gaussian background, and applications to pulsar timing.

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Section: Population Inferencementioning

confidence: 99%

Listening for the Cosmic Hum of Black Holes

Cornish

2018

Physics

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Roughly every 2-10 min, a pair of stellar-mass black holes merge somewhere in the Universe. A small fraction of these mergers are detected as individually resolvable gravitational-wave events by advanced detectors such as LIGO and Virgo. The rest contribute to a stochastic background. We derive the statistically optimal search strategy (producing minimum credible intervals) for a background of unresolved binaries. Our method applies Bayesian parameter estimation to all available data. Using Monte Carlo simulations, we demonstrate that the search is both "safe" and effective: it is not fooled by instrumental artifacts such as glitches and it recovers simulated stochastic signals without bias. Given realistic assumptions, we estimate that the search can detect the binary black hole background with about 1 day of design sensitivity data versus ≈40 months using the traditional cross-correlation search. This framework independently constrains the merger rate and black hole mass distribution, breaking a degeneracy present in the cross-correlation approach. The search provides a unified framework for population studies of compact binaries, which is cast in terms of hyperparameter estimation. We discuss a number of extensions and generalizations, including application to other sources (such as binary neutron stars and continuous-wave sources), simultaneous estimation of a continuous Gaussian background, and applications to pulsar timing.

show abstract

Astrophysical model selection in gravitational wave astronomy

Cited by 60 publications

References 52 publications

Constraints on the Dynamical Environments of Supermassive Black-Hole Binaries Using Pulsar-Timing Arrays

Constraints on the Dynamical Environments of Supermassive Black-Hole Binaries Using Pulsar-Timing Arrays

Optimal Search for an Astrophysical Gravitational-Wave Background

Listening for the Cosmic Hum of Black Holes

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