Parameter estimation of spinning binary inspirals using Markov-chain Monte Carlo

TL;DR

This paper introduces an MCMC method to estimate parameters of spinning binary inspirals in gravitational-wave data, improving sampling efficiency and addressing challenges in parameter space exploration.

Contribution

The paper develops a novel MCMC approach tailored for spinning binary inspiral parameter estimation in gravitational-wave signals, enhancing analysis capabilities.

Findings

Demonstrated the effectiveness of the MCMC method with sample runs.

Identified challenges in sampling the parameter space.

Showed potential for improved gravitational-wave data analysis.

Abstract

We present a Markov-chain Monte-Carlo (MCMC) technique to study the source parameters of gravitational-wave signals from the inspirals of stellar-mass compact binaries detected with ground-based gravitational-wave detectors such as LIGO and Virgo, for the case where spin is present in the more massive compact object in the binary. We discuss aspects of the MCMC algorithm that allow us to sample the parameter space in an efficient way. We show sample runs that illustrate the possibilities of our MCMC code and the difficulties that we encounter.