Python code for learning cosmology using different methods (MCMC, Gaussian processes, artificial neural networks, Bayesian ridge regression, genetic algorithm) and simulations (cosmic chronometers, supernovae Ia, baryon acoustic oscillations, RSD growth rates, GW bright sirens) based on real data. See notebook cosmo_tutorial.ipynb (or minimal_example.py) for basic usage; and arXiv:2508.20971 [astro-ph.CO] for more details.
Please cite the above paper when using cosmo-learn.
Tested on Linux, Mac and Windows WSL
Dependencies: python 3.10
Recommended Installation (in a conda environment): conda env create -f cosmo_learn.yml
Easy Installation: pip install cosmo-learn
- New data sets
- New methods
- New models
@article{Bernardo:2025pua,
author = "Bernardo, Reginald Christian and Grand{\'o}n, Daniela and Levi Said, Jackson and C{\'a}rdenas, V{\'\i}ctor H. and Belinario, Gene Carlo and Reyes, Reinabelle",
title = "{Cosmo-Learn: code for learning cosmology using different methods and mock data}",
eprint = "2508.20971",
archivePrefix = "arXiv",
primaryClass = "astro-ph.CO",
month = "8",
year = "2025"
}