Parameter estimation for signals from compact binary inspirals injected into LIGO data

TL;DR

This paper presents a Bayesian parameter estimation method for gravitational wave signals from compact binary inspirals injected into LIGO data, achieving high accuracy in recovering source parameters.

Contribution

It introduces a Markov-chain Monte-Carlo approach for realistic parameter estimation of signals in real detector noise, including spin effects.

Findings

Estimated chirp mass with 1-3% accuracy

Estimated mass ratio with 8-20% accuracy

Identified bias due to waveform model differences

Abstract

During the fifth science run of the Laser Interferometer Gravitational-wave Observatory (LIGO), signals modelling the gravitational waves emitted by coalescing non-spinning compact-object binaries were injected into the LIGO data stream. We analysed the data segments into which such injections were made using a Bayesian approach, implemented as a Markov-chain Monte-Carlo technique in our code SPINspiral. This technique enables us to determine the physical parameters of such a binary inspiral, including masses and spin, following a possible detection trigger. For the first time, we publish the results of a realistic parameter-estimation analysis of waveforms embedded in real detector noise. We used both spinning and non-spinning waveform templates for the data analysis and demonstrate that the intrinsic source parameters can be estimated with an accuracy of better than 1-3% in the chirp mass and 0.02-0.05 (8-20%) in the symmetric mass ratio if non-spinning waveforms are used. We also find a bias between the injected and recovered parameters, and attribute it to the difference in the post-Newtonian orders of the waveforms used for injection and analysis.