https://github.com/cosmostatgw/chimera
Combined Hierarchical Inference Model for Electromagnetic and gRavitational Wave Analysis
Science Score: 49.0%
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○CITATION.cff file
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✓.zenodo.json file
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✓DOI references
Found 1 DOI reference(s) in README -
✓Academic publication links
Links to: arxiv.org -
○Academic email domains
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○Scientific vocabulary similarity
Low similarity (15.6%) to scientific vocabulary
Keywords
Repository
Combined Hierarchical Inference Model for Electromagnetic and gRavitational Wave Analysis
Basic Info
- Host: GitHub
- Owner: CosmoStatGW
- License: gpl-3.0
- Language: Python
- Default Branch: main
- Homepage: https://chimera-gw.readthedocs.io/
- Size: 17.5 MB
Statistics
- Stars: 14
- Watchers: 3
- Forks: 0
- Open Issues: 0
- Releases: 1
Topics
Metadata Files
README.md
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.
The code is designed to be accurate for different scenarios, encompassing bright, dark, and spectral sirens methods, and computationally efficient in view of next-generation GW observatories and galaxy surveys. It uses the LAX-backend implementation and Just In Time (JIT) computation capabilities of JAX.
Installation
The code can be quikly installed from Pypi:
pip install chimera-gw
For more flexibility, clone the source repository into your working folder and install it locally:
git clone https://github.com/CosmoStatGW/CHIMERA
cd CHIMERA/
pip install -e .
To test the installation, run the following command:
python -c "import CHIMERA; print(CHIMERA.__version__)"
You can also run CHIMERA on GPU, but you have to install JAX with GPU support as explained in the JAX installation guide.
Documentation
The full documentation is provided at chimera-gw.readthedocs.io
Citation
If you find this code useful in your research, please cite the following paper (ADS, arXiv, INSPIRE):
@ARTICLE{2024ApJ...964..191B,
author = {{Borghi}, Nicola and {Mancarella}, Michele and {Moresco}, Michele and et al.},
title = "{Cosmology and Astrophysics with Standard Sirens and Galaxy Catalogs in View of Future Gravitational Wave Observations}",
journal = {\apj},
keywords = {Observational cosmology, Gravitational waves, Cosmological parameters, 1146, 678, 339, Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - Astrophysics of Galaxies, General Relativity and Quantum Cosmology},
year = 2024,
month = apr,
volume = {964},
number = {2},
eid = {191},
pages = {191},
doi = {10.3847/1538-4357/ad20eb},
archivePrefix = {arXiv},
eprint = {2312.05302},
primaryClass = {astro-ph.CO},
adsurl = {https://ui.adsabs.harvard.edu/abs/2024ApJ...964..191B},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
Owner
- Name: CosmoStatGW
- Login: CosmoStatGW
- Kind: organization
- Repositories: 2
- Profile: https://github.com/CosmoStatGW
A collection of public tools for cosmology with gravitational waves. Maintained by the GW group at the DPT of Theoretical Physics of the University of Geneva
GitHub Events
Total
- Watch event: 2
- Delete event: 1
- Push event: 4
Last Year
- Watch event: 2
- Delete event: 1
- Push event: 4
Packages
- Total packages: 1
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Total downloads:
- pypi 21 last-month
- Total dependent packages: 0
- Total dependent repositories: 0
- Total versions: 4
- Total maintainers: 1
pypi.org: chimera-gw
Combined Hierarchical Inference Model for Electromagnetic and gRavitational-wave Analysis
- Homepage: https://github.com/CosmoStatGW/CHIMERA
- Documentation: https://gwfast.readthedocs.io/en/latest/
- License: MIT
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Latest release: 1.0.3
published over 2 years ago
Rankings
Maintainers (1)
Dependencies
- h5py *
- healpy *
- jax *
- matplotlib *
- myst-nb *
- numpy *
- scikit-learn *
- scipy *
- sphinx-book-theme *
- sphinx-copybutton *
- astropy ^5.2
- docutils ^0.17.1
- h5py ^3.5
- healpy ^1.14
- jax *
- matplotlib ^3.4
- myst-parser ^0.18.1
- nbsphinx >=0.8.10
- numpy >=1.16
- python >=3.8
- readthedocs-sphinx-search ^0.1.2
- requests ^2.31.0
- schwimmbad ^0.3.2
- scikit-learn >=1.0
- scipy >=1.7
- sphinx 5.3.0
- sphinx-book-theme >=1.0
- sphinx-copybutton ^0.5.1