CHIMERA
CHIMERA is a hierarchical Bayesian pipeline for standard siren cosmology with gravitational wave data alone or in combination with galaxy catalogs.
The latest version delivers 10-1000× speedup through JAX and GPU acceleration, enabling the analysis of thousands of events for next-generation gravitational wave observatories.
Documentation
The full documentation is hosted at chimera-gw.readthedocs.io
Quick start 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__)"
These instructions install CHIMERA with a CPU-only version of JAX. For GPU support, first install JAX with a GPU backend following the official installation guide), then proceed with the CHIMERA installation.
For HPC systems with GPU nodes, see the HPC and GPU installation guide.
Citation
If you find this code useful in your research, please cite the following papers:
BibTeX from INSPIRE:
@article{Borghi:2023opd,
author = "Borghi, Nicola and Mancarella, Michele and Moresco, Michele and Tagliazucchi, Matteo and Iacovelli, Francesco and Cimatti, Andrea and Maggiore, Michele",
title = "{Cosmology and Astrophysics with Standard Sirens and Galaxy Catalogs in View of Future Gravitational Wave Observations}",
eprint = "2312.05302",
archivePrefix = "arXiv",
primaryClass = "astro-ph.CO",
doi = "10.3847/1538-4357/ad20eb",
journal = "Astrophys. J.",
volume = "964",
number = "2",
pages = "191",
year = "2024"
}
@article{Tagliazucchi:2025ofb,
author = "Tagliazucchi, Matteo and Moresco, Michele and Borghi, Nicola and Fiebig, Manfred",
title = "{Accelerating the Standard Siren Method: Improved Constraints on Modified Gravitational Wave Propagation with Future Data}",
eprint = "2504.02034",
archivePrefix = "arXiv",
primaryClass = "astro-ph.CO",
month = "4",
year = "2025"
}
Contributions
CHIMERA is actively maintained at the University of Bologna by: Nicola Borghi (nicola.borghi6@unibo.it), Matteo Tagliazucchi (matteo.tagliazucchi2@unibo.it), and Michele Moresco (michele.moresco@unibo.it).
Michele Mancarella, Francesco Iacovelli and Michele Maggiore contributed to the development of the first version of the code.
The development of CHIMERA has also been supported from the work of Master's thesis students at the University of Bologna (in reverse chronological order):
- Giulia Cuomo (2025, thesis): incompleteness function and application to GWTC-3 data
- Manfred Fiebig (2025, thesis): modified GW propagation function and forecasts for LVK-O5
- Niccolò Passaleva (2024, thesis): mass function models and inference with nested sampling
- Matteo Schulz (2024, thesis): mass function models and cosmological analysis
Metadata
Release files for chimera-gw 2.0.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| chimera_gw-2.0.0.tar.gz | 53.6 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| chimera_gw-2.0.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 115.0 kB
Release files / chimera_gw-2.0.0.tar.gz
| Download URL | chimera_gw-2.0.0.tar.gz |
|---|---|
| Size | 53.6 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
4d69da46d0d2204e95a57b85e3d1c02819bba65dabfd11e7ed447bb58f709643
|
|
BLAKE2b-256 checksum How to use checksums |
3d1a5535638451aa55dc22cea9f579657d7df8d3729167956cf93277ad5efaf4
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/5.0.0 CPython/3.12.3
|
Release files / chimera_gw-2.0.0-py3-none-any.whl
| Download URL | chimera_gw-2.0.0-py3-none-any.whl |
|---|---|
| Size | 61.5 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
b5b3f412edbc06efc6bd0d8ffbc49727c1f4c874fbf3a3622bee48182e393956
|
|
BLAKE2b-256 checksum How to use checksums |
b9f7c419a46ad2ceac93d8668f41d5cf1e26145b293d2e56b3259dce9b147bcb
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/5.0.0 CPython/3.12.3
|