Constraints On The Dynamical Environments Of Supermassive Black-hole Binaries Using Pulsar-timing Arrays

TL;DR

This paper presents a novel Bayesian method using Gaussian process regression to analyze pulsar timing array data, enabling inference of supermassive black-hole binary environment parameters from gravitational-wave signals.

Contribution

It introduces a new technique that emulates the strain spectrum of a stochastic background for improved astrophysical parameter inference in PTA data.

Findings

Demonstrates the method on mock data

Assesses inference prospects with NANOGrav-like data

Interpolates over binary environment parameters

Abstract

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.