Bilby: A User-friendly Bayesian Inference Library forGravitational-wave Astronomy
Monash Health · ARC Centre of Excellence for Gravitational Wave Discovery · +10 more institutions
Abstract
Abstract Bayesian parameter estimation is fast becoming the language of gravitational-wave astronomy. It is the method by which gravitational-wave data is used to infer the sources’ astrophysical properties. We introduce a user-friendly Bayesian inference library for gravitational-wave astronomy, B ilby . This P ython code provides expert-level parameter estimation infrastructure with straightforward syntax and tools that facilitate use by beginners. It allows users to perform accurate and reliable gravitational-wave parameter estimation on both real, freely available data from LIGO/Virgo and simulated data. We provide a suite of examples for the analysis of compact binary mergers and other types of signal…
Citation impact
- FWCI
- 50.44
- Percentile
- 100%
- References
- 74
Authors
21- GAGregory AshtonCorresponding
Monash Health, ARC Centre of Excellence for Gravitational Wave Discovery, Monash University
- MHMoritz HübnerCorresponding
Monash Health, ARC Centre of Excellence for Gravitational Wave Discovery, Monash University
- PDPaul D. LaskyCorresponding
Monash Health, ARC Centre of Excellence for Gravitational Wave Discovery, Monash University
- CTColm TalbotCorresponding
Monash Health, ARC Centre of Excellence for Gravitational Wave Discovery, Monash University
- KAKendall Ackley
Monash Health, ARC Centre of Excellence for Gravitational Wave Discovery, Monash University
Topics & keywords
- Binary number
- Bayesian probability
- Suite
- Population
- Binary data
- Inference
- Syntax
- Bayesian inference