Painting intracluster gas on gravity-only simulations
picasso is a model that allows making predictions for the thermodynamic properties of the gas in massive dark matter halos from gravity-only cosmological simulations.
It combines an analytical model of gas properties as a function of gravitational potential with a neural network predicting the parameters of said model.
It is released here as a Python package, combining an implementation of the gas model based on JAX and Flax, and models that have been pre-trained to reproduce gas properties from hydrodynamic simulations.
See also Kéruzoré et al. (2024).
picasso can be install via pip:
pip install -e "git+https://github.com/fkeruzore/picasso.git#egg=picasso[jax]"Alternatively, if you already have JAX and flax installed, you may use
pip install -e "git+https://github.com/fkeruzore/picasso.git#egg=picasso"The latter option will not install or upgrade any package relying on JAX, which can be useful to avoid messing up an existing install. To install JAX on your system, see JAX's installation page.
picasso uses Poetry to manage dependencies.
To test your installation of picasso, you can install the tests dependency group and run pytest:
git clone [email protected]:fkeruzore/picasso.git
cd picasso
poetry install --with=tests
poetry run pytestSome of the test also include basic benchmarking of model predictions using pytest-benchmark:
poetry run pytest --benchmark-enableIf you use picasso for your research, please cite the picasso original paper:
@article{keruzore_picasso_2024,
title={The picasso gas model: Painting intracluster gas on gravity-only simulations},
author={F. Kéruzoré and L. E. Bleem and N. Frontiere and N. Krishnan and M. Buehlmann and J. D. Emberson and S. Habib and P. Larsen},
year={2024},
eprint={2408.17445},
archivePrefix={arXiv},
primaryClass={astro-ph.CO},
url={https://arxiv.org/abs/2408.17445},
}