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Constraints On The Dynamical Environments Of Supermassive Black-hole Binaries Using Pulsar-timing Arrays
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Constraints On The Dynamical Environments Of Supermassive Black-hole Binaries Using Pulsar-timing Arrays
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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 using 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.
Forward citations
Cited by 2 Pith papers
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The Heavy Tailed Non-Gaussianity of the Supermassive Black Hole Gravitational Wave Background
The gravitational wave background from supermassive black hole binaries has a universal heavy-tailed amplitude distribution with power-law index -4, causing divergent higher moments and dominance of the strongest sign...
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Inferring the properties of a population of compact binaries in presence of selection effects
Pedagogical derivation from first principles of hierarchical Bayesian inference for population properties of compact binaries in the presence of selection effects, with two worked examples.
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