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

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.

References in corpus (11)

• LIGO: The Laser Interferometer Gravitational-Wave Observatory
• Comparison of post-Newtonian templates for compact binary inspiral signals in gravitational-wave detectors
• Gravitational-Wave Astronomy with Inspiral Signals of Spinning Compact-Object Binaries
• Parameter estimation of spinning binary inspirals using Markov-chain Monte Carlo
• Phasing of gravitational waves from inspiralling eccentric binaries at the third-and-a-half post-Newtonian order
• A Bayesian Approach to the Detection Problem in Gravitational Wave Astronomy
• Coherent Bayesian inference on compact binary inspirals using a network of interferometric gravitational wave detectors
• Degeneracies in Sky Localisation Determination from a Spinning Coalescing Binary through Gravitational Wave Observations: a Markov-Chain Monte-Carlo Analysis for two Detectors
• Parameter estimation on compact binary coalescences with abruptly terminating gravitational waveforms
• Parameter estimation for signals from compact binary inspirals injected into LIGO data
• A more effective coordinate system for parameter estimation of precessing compact binaries from gravitational waves

Cited by in corpus (5)

• BayesLine: Bayesian Inference for Spectral Estimation of Gravitational Wave Detector Noise
• Parameter estimation for binary neutron-star coalescences with realistic noise during the Advanced LIGO era
• High-energy electromagnetic offline follow-up of LIGO-Virgo gravitational-wave binary coalescence candidate events
• Distinguishing types of compact-object binaries using the gravitational-wave signatures of their mergers
• A New Method of Accelerated Bayesian Inference for Comparable Mass Binaries in both Ground and Space-Based Gravitational Wave Astronomy