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• W2561084811 abstract "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 pulsar timing arrays specifically, we interpolate over the parameter space of supermassive black-hole binary environments, including three-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." @default.
• W2561084811 created "2017-01-06" @default.
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• W2561084811 date "2017-05-03" @default.
• W2561084811 modified "2023-10-09" @default.
• W2561084811 title "Constraints on the Dynamical Environments of Supermassive Black-Hole Binaries Using Pulsar-Timing Arrays" @default.
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• W2561084811 doi "https://doi.org/10.1103/physrevlett.118.181102" @default.
• W2561084811 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/28524688" @default.
• W2561084811 hasPublicationYear "2017" @default.
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