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*******
CHIMERA
*******

**CHIMERA** (Combined Hierarchical Inference Model for Electromagnetic and gRavitational-wave Analysis), is a flexible Python code to analyze standard sirens with galaxy catalogs, allowing for a joint fitting of the cosmological and astrophysical population parameters within a Hierarchical Bayesian Inference framework. 


.. image:: https://img.shields.io/badge/GitHub-CHIMERA-9e8ed7
    :target: https://github.com/CosmoStatGW/CHIMERA/
    :alt: GitHub
.. image:: https://img.shields.io/badge/arXiv-2106.14894-28bceb
    :target: https://arxiv.org/abs/2106.14894
    :alt: arXiv
.. image:: https://readthedocs.org/projects/chimera-gw/badge/?version=latest
    :target: https://chimera-gw.readthedocs.io/latest/?badge=latest
    :alt: Documentation Status
.. image:: https://img.shields.io/badge/license-MIT-fb7e21
    :target: https://github.com/CosmoStatGW/CHIMERA/blob/main/LICENSE
    :alt: License
.. image:: https://img.shields.io/gitlab/v/release/14528131
    :target: https://gitlab.com/mmoresco/CHIMERA/-/tags
    :alt: Release

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Installation
------------

**CHIMERA** can be installed using `Pypi <https://pypi.org/project/chimera-gw>`_:

.. code-block:: bash

   pip install chimera-gw


License & Attribution
---------------------

**CHIMERA** is free software made available under the MIT License. For details see the ``LICENSE``.

If you find this code useful in your research, please cite the following paper (`ADS <https://ui.adsabs.harvard.edu/abs/2022ApJ...927..164B/abstract>`_, `arXiv <https://arxiv.org/abs/2106.14894>`_, `INSPIRE <https://inspirehep.net/literature/1871797>`_):

.. code-block:: tex

    @ARTICLE{Borghi2022a,
        author = {{Borghi}, Nicola and {Moresco}, Michele and {Cimatti}, Andrea and et al.},
         title = "{Toward a Better Understanding of Cosmic Chronometers: Stellar Population Properties of Passive Galaxies at Intermediate Redshift}",
       journal = {ApJ},
          year = 2022,
         month = mar,
        volume = {927},
         pages = {164},
           doi = {10.3847/1538-4357/ac3240},
        eprint = {2106.14894},
        adsurl = {https://ui.adsabs.harvard.edu/abs/2022ApJ...927..164B},
    }


The team
--------

**Core team**

- `Nicola Borghi <https://orcid.org/0000-0002-2889-8997>`_ (**main developer**, nicola.borghi6@unibo.it)
- `Michele Mancarella <https://orcid.org/0000-0002-0675-508X>`_ (michele.mancarella@unimib.it) 
- `Michele Moresco <https://orcid.org/0000-0002-7616-7136>`_ (michele.moresco@unibo.it) 

**Contributors**

- `Matteo Tagliazucchi <https://orcid.org/0000-0002-2889-8997>`_
- Niccolò Passaleva
- `Francesco Iacovelli <https://orcid.org/0000-0002-4875-5862>`_

The code was developed starting from https://github.com/CosmoStatGW/DarkSirensStat (`Finke et al. 2019 <https://orcid.org/0000-0002-2889-8997>`_) and https://github.com/CosmoStatGW/MGCosmoPop (`Mancarella et al. 2021 <https://orcid.org/0000-0002-2889-8997>`_).


Documentation
-------------

.. toctree::
    :maxdepth: 1
    :caption: User Guide

    user_guide/installation
    user_guide/introduction
    user_guide/getting_started
    user_guide/framework
    user_guide/changelog
    user_guide/citing

.. toctree::
   :maxdepth: 1
   :caption: Tutorials

   tutorials/GW170817

.. toctree::
    :maxdepth: 1
    :caption: Python APIs

    api/MCMC
    api/Likelihood
    api/GW
    api/EM 
    api/Bias
    api/Models





.. Changelog
.. ---------

.. .. include:: changelog.rst


.. TO BULD THE DOCS
   python -m sphinx -T -E -b html -d _build/doctrees -D language=en . _build/html
   python -m sphinx -b latex -D language=en -d _build/doctrees . _build/latex