Maqola

Bilby: A User-friendly Bayesian Inference Library for Gravitational-wave Astronomy

G. AshtonSchool of Physics and Astronomy, Monash University, Clayton, VIC 3800, AustraliaM. T. HübnerSchool of Physics and Astronomy, Monash University, Clayton, VIC 3800, AustraliaP. D. LaskyOzGrav: The ARC Centre of Excellence for Gravitational Wave Discovery, Clayton, VIC 3800, AustraliaColm TalbotSchool of Physics and Astronomy, Monash University, Clayton, VIC 3800, AustraliaK. AckleySchool of Physics and Astronomy, Monash University, Clayton, VIC 3800, AustraliaS. BiscoveanuLIGO, Massachusetts Institute of Technology, Cambridge, MA 02139, USAQi ChuSchool of Physics, University of Western Australia, Crawley, WA 6009, AustraliaAtul K. DivakarlaOzGrav: The ARC Centre of Excellence for Gravitational Wave Discovery, Clayton, VIC 3800, AustraliaP. J. EasterSchool of Physics and Astronomy, Monash University, Clayton, VIC 3800, AustraliaB. GoncharovOzGrav: The ARC Centre of Excellence for Gravitational Wave Discovery, Clayton, VIC 3800, AustraliaF VivancoOzGrav: The ARC Centre of Excellence for Gravitational Wave Discovery, Clayton, VIC 3800, AustraliaJ. HarmsGran Sasso Science Institute (GSSI), I-67100 L'Aquila, ItalyMarcus E. LowerCentre for Astrophysics and Supercomputing, Swinburne University of Technology, Hawthorn, VIC 3122, AustraliaG. D. MeadorsOzGrav: The ARC Centre of Excellence for Gravitational Wave Discovery, Clayton, VIC 3800, AustraliaD. A. MelchorCalifornia State University Fullerton, Fullerton, CA 92831, USAEthan PayneOzGrav: The ARC Centre of Excellence for Gravitational Wave Discovery, Clayton, VIC 3800, AustraliaM. PitkinSUPA, School of Physics & Astronomy, University of Glasgow, Glasgow G12 8QQ, UKJ. PowellSwinburne University of TechnologyNikhil SarinOzGrav: The ARC Centre of Excellence for Gravitational Wave Discovery, Clayton, VIC 3800, AustraliaR. J. E. SmithSchool of Physics and Astronomy, Monash University, Clayton, VIC 3800, AustraliaE. ThraneSchool of Physics and Astronomy, Monash University, Clayton, VIC 3800, Australia

2024en

ABI

Annotatsiya

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, including supernovae and the remnants of binary neutron star mergers. These examples illustrate how to change the signal model, implement new likelihood functions, and add new detectors. BILBY has additional functionality to do population studies using hierarchical Bayesian modeling. We provide an example in which we infer the shape of the black hole mass distribution from an ensemble of observations of binary black hole mergers.

Hali tarjima qilinmagan

Identifikatorlar

DOI: 10.25916/sut.26325967.v1

Iqtiboslar va manbalar

2 ta iqtibos0 ta foydalanilgan manba