Bajes: Bayesian inference of multimessenger astrophysical data, methods and application to gravitational-waves
In 2102.00017 we introduce bajes [baɪɛs], a Python package for Bayesian inference developed at Friedrich-Schiller-Universtät Jena and specialized in the analysis of gravitational-wave and multi-messenger transients. The software is designed to be state-of-art, simple-to-use and light-weighted with minimal dependencies on external libraries.
We describe the general workflow and the parameter estimation pipeline for compact-binary-coalescence gravitational-wave transients. The latter is validated against injections of binary black hole and binary neutron star waveforms.
As a full scale application, we re-analyze the GWTC-1 transients using the effective-one-body TEOBResumS approximant and the posterior samples are available on Zenodo.org.
bajes is publicly available on github.
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