Bayesian inference on EMRI signals using low frequency approximations

TL;DR

This paper introduces a Bayesian inference method using advanced MCMC algorithms to detect and estimate parameters of EMRI gravitational wave signals in LISA data, demonstrating promising results in simulated scenarios.

Contribution

It presents the first application of a fully Markovian Bayesian algorithm for EMRI searches, applicable to various gravitational wave detectors.

Findings

Successfully recovered EMRI signals from simulated LISA data

Effective in handling multiple EMRI sources

Shows promise for realistic data analysis

Abstract

Extreme mass ratio inspirals (EMRIs) are thought to be one of the most exciting gravitational wave sources to be detected with LISA. Due to their complicated nature and weak amplitudes the detection and parameter estimation of such sources is a challenging task. In this paper we present a statistical methodology based on Bayesian inference in which the estimation of parameters is carried out by advanced Markov chain Monte Carlo (MCMC) algorithms such as parallel tempering MCMC. We analysed high and medium mass EMRI systems that fall well inside the low frequency range of LISA. In the context of the Mock LISA Data Challenges, our investigation and results are also the first instance in which a fully Markovian algorithm is applied for EMRI searches. Results show that our algorithm worked well in recovering EMRI signals from different (simulated) LISA data sets having single and multiple EMRI sources and holds great promise for posterior computation under more realistic conditions. The search and estimation methods presented in this paper are general in their nature, and can be applied in any other scenario such as AdLIGO, AdVIRGO and Einstein Telescope with their respective response functions.