Maqola

Bilby: A User-friendly Bayesian Inference Library forGravitational-wave Astronomy

Gregory AshtonMonash UniversityMoritz HübnerOzGrav: The ARC Centre of Excellence for Gravitational Wave Discovery, Clayton, VIC 3800, AustraliaP. D. LaskyMonash UniversityColm TalbotSchool of Physics and Astronomy, Monash University, Clayton, VIC 3800, Australia; [email protected], [email protected], [email protected], [email protected]K. AckleyOzGrav: The ARC Centre of Excellence for Gravitational Wave Discovery, Clayton, VIC 3800, AustraliaSylvia BiscoveanuMassachusetts Institute of TechnologyQi ChuOzGrav: The ARC Centre of Excellence for Gravitational Wave Discovery, Crawley, WA 6009, AustraliaAtul DivakarlaMonash UniversityPaul J. EasterOzGrav: The ARC Centre of Excellence for Gravitational Wave Discovery, Clayton, VIC 3800, AustraliaBoris GoncharovMonash UniversityFrancisco Hernandez VivancoMonash UniversityJan HarmsINFN, Laboratori Nazionali del Gran Sasso, I-67100 Assergi, ItalyMarcus E. LowerSchool of Physics and Astronomy, Monash University, Clayton, VIC 3800, Australia; [email protected], [email protected], [email protected], [email protected]Grant D. MeadorsMonash UniversityDenyz MelchorMonash UniversityEthan 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, UKJade PowellCentre for Astrophysics and Supercomputing, Swinburne University of Technology, Hawthorn, VIC 3122, AustraliaNikhil SarinSchool of Physics and Astronomy, Monash University, Clayton, VIC 3800, Australia; [email protected], [email protected], [email protected], [email protected]Rory J. E. SmithMonash UniversityEric ThraneSchool of Physics and Astronomy, Monash University, Clayton, VIC 3800, Australia; [email protected], [email protected], [email protected], [email protected]

2019en

Ochiq kirishBronza

ABI

Annotatsiya

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 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. B ilby 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: 10.3847/1538-4365/ab06fc

Iqtiboslar va manbalar

5 ta iqtibos0 ta foydalanilgan manba