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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.
Pulsar timing arrays (PTAs) are expected to detect gravitational waves (GWs) from individual low-redshift (z<1.5) compact supermassive (M>10^9 Msun) black hole (SMBH) binaries with orbital periods of approx. 0.1 - 10 yrs. Identifying the electromagne
Precision timing of large arrays (>50) of millisecond pulsars will detect the nanohertz gravitational-wave emission from supermassive binary black holes within the next ~3-7 years. We review the scientific opportunities of these detections, the requi
We estimate the merger timescale of spectroscopically-selected, subparsec supermassive black hole binary (SMBHB) candidates by comparing their expected contribution to the gravitational wave background (GWB) with the sensitivity of current pulsar tim
Pulsar timing observations are used to place constraints on the rate of coalescence of supermassive black-hole (SMBH) binaries as a function of mass and redshift. In contrast to the indirect constraints obtained from other techniques, pulsar timing o
The North American Nanohertz Observatory for Gravitational Waves (NANOGrav) project currently observes 43 pulsars using the Green Bank and Arecibo radio telescopes. In this work we use a subset of 17 pulsars timed for a span of roughly five years (20