Robust parameter estimation for compact binaries with ground-based gravitational-wave observations using the LALInference software library

TL;DR

This paper introduces the LALInference software library for Bayesian parameter estimation of compact binary gravitational wave signals, demonstrating its accuracy, efficiency, and ability to recover source parameters from simulated data across various binary configurations.

Contribution

The paper presents a well-tested, versatile software toolkit for gravitational wave parameter estimation, capable of handling complex spin configurations and validated with simulated data.

Findings

Accurately recovers parameters from simulated GW signals.

Consistent results across different sampling algorithms.

Bayesian credible intervals match frequentist confidence intervals.

Abstract

The Advanced LIGO and Advanced Virgo gravitational wave (GW) detectors will begin operation in the coming years, with compact binary coalescence events a likely source for the first detections. The gravitational waveforms emitted directly encode information about the sources, including the masses and spins of the compact objects. Recovering the physical parameters of the sources from the GW observations is a key analysis task. This work describes the LALInference software library for Bayesian parameter estimation of compact binary signals, which builds on several previous methods to provide a well-tested toolkit which has already been used for several studies. We show that our implementation is able to correctly recover the parameters of compact binary signals from simulated data from the advanced GW detectors. We demonstrate this with a detailed comparison on three compact binary systems: a binary neutron star, a neutron star black hole binary and a binary black hole, where we show a cross-comparison of results obtained using three independent sampling algorithms. These systems were analysed with non-spinning, aligned spin and generic spin configurations respectively, showing that consistent results can be obtained even with the full 15-dimensional parameter space of the generic spin configurations. We also demonstrate statistically that the Bayesian credible intervals we recover correspond to frequentist confidence intervals under correct prior assumptions by analysing a set of 100 signals drawn from the prior. We discuss the computational cost of these algorithms, and describe the general and problem-specific sampling techniques we have used to improve the efficiency of sampling the compact binary coalescence parameter space.