LISA source confusion: identification and characterization of signals

TL;DR

This paper introduces a Bayesian MCMC-based method for identifying and characterizing multiple gravitational wave signals from compact binaries in LISA data, outperforming classical spectral techniques.

Contribution

It presents a novel Bayesian inference approach using MCMC to detect and estimate parameters of multiple signals without prior knowledge of their number.

Findings

Accurately estimates the number of signals in noisy data

Provides precise parameter estimation for each signal

Outperforms classical spectral methods in detection accuracy

Abstract

The Laser Interferometer Space Antenna (LISA) is expected to detect gravitational radiation from a large number of compact binary systems. We present a method by which these signals can be identified and have their parameters estimated. Our approach uses Bayesian inference, specifically the application of a Markov chain Monte Carlo method. The simulation study that we present here considers a large number of sinusoidal signals in noise, and our method estimates the number of periodic signals present in the data, the parameters for these signals and the noise level. The method is significantly better than classical spectral techniques at performing these tasks and does not use stopping criteria for estimating the number of signals present.