Inference on inspiral signals using LISA MLDC data

TL;DR

This paper introduces a Bayesian inference framework utilizing MCMC to analyze LISA data for binary inspiral signals, enabling parameter estimation within a 9-dimensional space based on MLDC data.

Contribution

The paper develops and demonstrates a Bayesian MCMC approach for extracting inspiral signal parameters from LISA data, tailored to the MLDC 1.2 challenge.

Findings

Successful parameter estimation from simulated LISA data

Effective exploration of a 9-dimensional parameter space

Potential for improved gravitational wave data analysis

Abstract

In this paper we describe a Bayesian inference framework for analysis of data obtained by LISA. We set up a model for binary inspiral signals as defined for the Mock LISA Data Challenge 1.2 (MLDC), and implemented a Markov chain Monte Carlo (MCMC) algorithm to facilitate exploration and integration of the posterior distribution over the 9-dimensional parameter space. Here we present intermediate results showing how, using this method, information about the 9 parameters can be extracted from the data.