Bayesian inference for compact binary coalescences with BILBY: Validation and application to the first LIGO--Virgo gravitational-wave transient catalogue (2006.00714v4)

Abstract: Gravitational waves provide a unique tool for observational astronomy. While the first LIGO--Virgo catalogue of gravitational-wave transients (GWTC-1) contains eleven signals from black hole and neutron star binaries, the number of observations is increasing rapidly as detector sensitivity improves. To extract information from the observed signals, it is imperative to have fast, flexible, and scalable inference techniques. In a previous paper, we introduced BILBY: a modular and user-friendly Bayesian inference library adapted to address the needs of gravitational-wave inference. In this work, we demonstrate that BILBY produces reliable results for simulated gravitational-wave signals from compact binary mergers, and verify that it accurately reproduces results reported for the eleven GWTC-1 signals. Additionally, we provide configuration and output files for all analyses to allow for easy reproduction, modification, and future use. This work establishes that BILBY is primed and ready to analyse the rapidly growing population of compact binary coalescence gravitational-wave signals.

• The paper introduces Bilby as a modular Bayesian inference framework designed for compact binary coalescence analysis.
• The paper validates Bilby by comparing its performance on simulated signals and GWTC-1 events against established methods like LALInference.
• The paper details software enhancements such as tailored priors and distributed computing to enable rapid, scalable gravitational-wave analysis.

Bayesian Inference for Compact Binary Coalescences: Validation and Application

This paper presents a comprehensive analysis of Bayesian inference applied to compact binary coalescences using the library, Bilby. The research is significant due to the increasing number of gravitational-wave observations as detector sensitivities improve with tools such as LIGO and Virgo. The authors emphasize the importance of fast, flexible, and robust Bayesian inference techniques for extracting detailed information from gravitational-wave signals. Here, they demonstrate Bilby’s capability to provide reliable parameter estimation for compact binary mergers and validate its performance with previous observations.

Key Contributions

1. Framework of Bilby: The paper outlines Bilby, a user-friendly library designed for gravitational-wave inference via Bayesian parameter estimation. Emphasis is placed on its modular nature, allowing adaptation for different inference problems in gravitational-wave astronomy.
2. Method Validation: The authors validate Bilby against simulated gravitational-wave signals and real events from the LIGO-Virgo gravitational-wave transient catalogue (GWTC-1). Results show Bilby provides consistent and reliable posteriors for parameter estimation compared to other established methods.
3. Implementation Details: This paper details improvements made to the Bilby software, including constrained and conditional prior distributions, cosmological priors, and various boundary conditions for parameter estimation. It further discusses the integration of marginalization techniques to improve computation speed.
4. Analysis of GWTC-1 Events: The research reanalyses events in GWTC-1, verifying the accuracy of Bilby against results from the LVC Bayesian parameter estimation package LALInference, demonstrating its efficacy in handling large numbers of compact binary coalescences rapidly.
5. Performance and Scalability: Bilby shows excellent scalability with its computation distributed across CPUs, significantly reducing analysis time for parameter estimation tasks. This is valuable for the rapid characterization of numerous gravitational-wave detections expected in the future.

Implications and Speculative Outlook

The paper underscores the pivotal role of parameter inference in gravitational-wave astronomy, providing insights into astrophysical phenomena such as binary stellar evolution and the neutron star equation of state. With more sensitive detectors on the horizon and frequent detections expected, automated and reliable inference methods like Bilby will become even more crucial.

Looking forward, as Bayesian inference methods such as Bilby are refined, they are expected to enhance our understanding of underlying astrophysical processes through better characterization of gravitational-wave transients. Future developments could include integrating machine learning approaches for real-time inference and optimization for handling non-Euclidean mixture space problems.

Bilby’s ability to incorporate sophisticated analytical techniques and adapt to user-specific needs promises ongoing applicability across broader domains as gravitational-wave research evolves. Researchers are poised to leverage such tools further to test theories of relativity or explore variances in binary mergers, thereby contributing to advancements in both theoretical and observational astrophysics.