# The CAMELS project: public data release

arXiv: https://arxiv.org/abs/2201.01300 · HTML: https://mlsanity.com/paper/2201.01300/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 109 references that are in the map.
- First posted on arXiv 4 Jan 2022.

## Builds on (most cited first, 11 of 11)

- [Learning to Predict the Cosmological Structure Formation](https://mlsanity.com/paper/1811.06533/) (2018)
- [The CAMELS Multifield Dataset: Learning the Universe's Fundamental Parameters with Artificial Intelligence](https://mlsanity.com/paper/2109.10915/) (2021)
- [AI-assisted super-resolution cosmological simulations](https://mlsanity.com/paper/2010.06608/) (2020)
- [Fast and Credible Likelihood-Free Cosmology with Truncated Marginal Neural Ratio Estimation](https://mlsanity.com/paper/2111.08030/) (2021)
- [Inferring halo masses with Graph Neural Networks](https://mlsanity.com/paper/2111.08683/) (2021)
- [Forging new worlds: high-resolution synthetic galaxies with chained generative adversarial networks](https://mlsanity.com/paper/1811.03081/) (2018)
- [Mass Estimation of Galaxy Clusters with Deep Learning I: Sunyaev-Zel'dovich Effect](https://mlsanity.com/paper/2003.06135/) (2020)
- [deep21: a Deep Learning Method for 21cm Foreground Removal](https://mlsanity.com/paper/2010.15843/) (2020)
- [AI-assisted super-resolution cosmological simulations II: Halo substructures, velocities and higher order statistics](https://mlsanity.com/paper/2105.01016/) (2021)
- [Hybrid analytic and machine-learned baryonic property insertion into galactic dark matter haloes](https://mlsanity.com/paper/2012.05820/) (2020)
- [Fast, high-fidelity Lyman α forests with convolutional neural networks](https://mlsanity.com/paper/2106.12662/) (2021)

## Built on by (most cited first, 6 of 6)

- [Robust Field-level Likelihood-free Inference with Galaxies](https://mlsanity.com/paper/2302.14101/) (2023)
- [Multilevel neural simulation-based inference](https://mlsanity.com/paper/2506.06087/) (2025)
- [Field-level simulation-based inference with galaxy catalogs: the impact of systematic effects](https://mlsanity.com/paper/2310.15234/) (2023)
- [Galaxy Phase-Space and Field-Level Cosmology: The Strength of Semi-Analytic Models](https://mlsanity.com/paper/2512.10222/) (2025)
- [Conservative neural posterior estimation via distributionally robust training](https://mlsanity.com/paper/2605.28516/) (2026)
- [ThousandWorlds: A benchmark for climate emulation of potentially habitable exoplanets](https://mlsanity.com/paper/2606.18338/) (2026)

## Abstract (arXiv)

The Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) project was developed to combine cosmology with astrophysics through thousands of cosmological hydrodynamic simulations and machine learning. CAMELS contains 4,233 cosmological simulations, 2,049 N-body and 2,184 state-of-the-art hydrodynamic simulations that sample a vast volume in parameter space. In this paper we present the CAMELS public data release, describing the characteristics of the CAMELS simulations and a variety of data products generated from them, including halo, subhalo, galaxy, and void catalogues, power spectra, bispectra, Lyman-α spectra, probability distribution functions, halo radial profiles, and X-rays photon lists. We also release over one thousand catalogues that contain billions of galaxies from CAMELS-SAM: a large collection of N-body simulations that have been combined with the Santa Cruz Semi-Analytic Model. We release all the data, comprising more than 350 terabytes and containing 143,922 snapshots, millions of halos, galaxies and summary statistics. We provide further technical details on how to access, download, read, and process the data at urlhttps://camels.readthedocs.io.