# The CAMELS Multifield Dataset: Learning the Universe's Fundamental Parameters with Artificial Intelligence

arXiv: https://arxiv.org/abs/2109.10915 · HTML: https://mlsanity.com/paper/2109.10915/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 40 references that are in the map.
- First posted on arXiv 22 Sep 2021.

## Builds on (most cited first, 7 of 7)

- [Learning to Predict the Cosmological Structure Formation](https://mlsanity.com/paper/1811.06533/) (2018)
- [AI-assisted super-resolution cosmological simulations](https://mlsanity.com/paper/2010.06608/) (2020)
- [Painting with baryons: augmenting N-body simulations with gas using deep generative models](https://mlsanity.com/paper/1903.12173/) (2019)
- [Learning effective physical laws for generating cosmological hydrodynamics with Lagrangian Deep Learning](https://mlsanity.com/paper/2010.02926/) (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)

## Built on by (most cited first, 18 of 18)

- [Learning cosmology and clustering with cosmic graphs](https://mlsanity.com/paper/2204.13713/) (2022)
- [Inferring halo masses with Graph Neural Networks](https://mlsanity.com/paper/2111.08683/) (2021)
- [Robust Field-level Likelihood-free Inference with Galaxies](https://mlsanity.com/paper/2302.14101/) (2023)
- [Can denoising diffusion probabilistic models generate realistic astrophysical fields?](https://mlsanity.com/paper/2211.12444/) (2022)
- [The CAMELS project: public data release](https://mlsanity.com/paper/2201.01300/) (2022)
- [Augmenting astrophysical scaling relations with machine learning: application to reducing the Sunyaev-Zeldovich flux-mass scatter](https://mlsanity.com/paper/2201.01305/) (2022)
- [Machine Learning and Cosmology](https://mlsanity.com/paper/2203.08056/) (2022)
- [Domain Adaptive Graph Neural Networks for Constraining Cosmological Parameters Across Multiple Data Sets](https://mlsanity.com/paper/2311.01588/) (2023)
- [Robust field-level inference with dark matter halos](https://mlsanity.com/paper/2209.06843/) (2022)
- [The CAMELS project: Expanding the galaxy formation model space with new ASTRID and 28-parameter TNG and SIMBA suites](https://mlsanity.com/paper/2304.02096/) (2023)
- [Tackling the Problem of Distributional Shifts: Correcting Misspecified, High-Dimensional Data-Driven Priors for Inverse Problems](https://mlsanity.com/paper/2407.17667/) (2024)
- [Field-level simulation-based inference with galaxy catalogs: the impact of systematic effects](https://mlsanity.com/paper/2310.15234/) (2023)
- [Cosmological Field Emulation and Parameter Inference with Diffusion Models](https://mlsanity.com/paper/2312.07534/) (2023)
- [Diffusion-HMC: Parameter Inference with Diffusion-model-driven Hamiltonian Monte Carlo](https://mlsanity.com/paper/2405.05255/) (2024)
- [Probabilistic reconstruction of Dark Matter fields from biased tracers using diffusion models](https://mlsanity.com/paper/2311.08558/) (2023)
- [Mapping Dark-Matter Clusters via Physics-Guided Diffusion Models](https://mlsanity.com/paper/2603.14503/) (2026)
- [Learnable wavelet neural networks for cosmological inference](https://mlsanity.com/paper/2307.14362/) (2023)
- [Towards out-of-distribution generalization in large-scale astronomical surveys: robust networks learn similar representations](https://mlsanity.com/paper/2311.18007/) (2023)

## Abstract (arXiv)

We present the Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) Multifield Dataset, CMD, a collection of hundreds of thousands of 2D maps and 3D grids containing many different properties of cosmic gas, dark matter, and stars from 2,000 distinct simulated universes at several cosmic times. The 2D maps and 3D grids represent cosmic regions that span ~100 million light years and have been generated from thousands of state-of-the-art hydrodynamic and gravity-only N-body simulations from the CAMELS project. Designed to train machine learning models, CMD is the largest dataset of its kind containing more than 70 Terabytes of data. In this paper we describe CMD in detail and outline a few of its applications. We focus our attention on one such task, parameter inference, formulating the problems we face as a challenge to the community. We release all data and provide further technical details at https://camels-multifield-dataset.readthedocs.io.