Bilby: A User-friendly Bayesian Inference Library for Gravitational-wave Astronomy
ARC Centre of Excellence for Gravitational Wave Discovery · Monash University · +9 more institutions
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, BILBY. This PYTHON 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 models,…
Citation impact
- FWCI
- 113.50
- Percentile
- 100%
- References
- 88
Authors
21- GAG. AshtonCorresponding
ARC Centre of Excellence for Gravitational Wave Discovery, Monash University
- MTM. T. HübnerCorresponding
ARC Centre of Excellence for Gravitational Wave Discovery, Monash University
- PDP. D. LaskyCorresponding
ARC Centre of Excellence for Gravitational Wave Discovery, Monash University
- CTC. TalbotCorresponding
ARC Centre of Excellence for Gravitational Wave Discovery, Monash University
- KAK. Ackley
ARC Centre of Excellence for Gravitational Wave Discovery, Monash University
Topics & keywords
- Gravitational wave
- Inference
- Bayesian inference
- Gravitational-wave astronomy
- Bayesian probability
- Astronomy
- User Friendly
- Computer science
- Industry, innovation and infrastructure