bajes [baɪɛs] is a Python software 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.
bajes is compatible with Python v3.7 (or higher)
and it is built on modules that can be easily installed via pip
.
The mandatory dependencies are numpy
, scipy
and astropy
.
However, the user might need to download some further packages.
See INSTALL
for more information.
bajes provides an homonymous Python module that includes:
bajes.inf
: implementation of the statistical objects and Bayesian workflow,bajes.obs
: tools and methods for data analysis of multi-messenger signals. For more details, visitgw_tutorial
.
The bajes package provides a user-friendly interface capable to easily set up a Bayesian analysis for an arbitrary model. Providing a prior file and a likelihood function, the command
python -m bajes -p prior.ini -l like.py -o /path/to/outdir/
will run a parameter estimation job, inferring the properties of the input model.
For more details, visit inf_tutorial
or type python -m bajes --help
.
The bajes infrastructure allows the user to set up a pipeline for parameters estimation of multi-messenger transients. This can be easily done writing a configuration file, that contains the information to be passed to the executables. Subsequently, the following command,
bajes_pipe.py config.ini
will generates the requested output directory, if it does not exists, and
the pipeline will be written into a bash executable (/path/to/outdir/jobname.sub
).
For more details, visit conifg_example
.
bajes is developed by Matteo Breschi at the Friedrich-Schiller-Universität Jena with the contribution of Rossella Gamba and Sebastiano Bernuzzi.
If you find bajes useful in your research, please include the following citation in your publication,
@article{Bajes:2021,
author = "Breschi, Matteo and Gamba, Rossella and Bernuzzi, Sebastiano",
title = "${\tt bajes}$: Bayesian inference of multimessenger astrophysical data,
methods and application to gravitational-waves",
eprint = "2102.00017",
archivePrefix = "arXiv",
primaryClass = "gr-qc",
month = "1",
year = "2021"}
See CREDITS
for more information.
bajes has benefited from open source libraries, including the samplers,
and the gravitational-wave analysis packages,
We also acknowledge the LIGO-Virgo-KAGRA Collaboration for maitaining the GWOSC archive.