mlsanityData to 7 Oct 2026

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

arXiv 2109.10915 ↗First posted 22 Sep 2021cs.LG astro-ph.CO astro-ph.GAcited by 18

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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.

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

Builds on

7 of 40 references.

References · 7

  1. 2018Learning to Predict the Cosmological Structure Formationcited by 38
  2. 2020AI-assisted super-resolution cosmological simulationscited by 15
  3. 2019Painting with baryons: augmenting N-body simulations with gas using deep generative modelscited by 7
  4. 2020Learning effective physical laws for generating cosmological hydrodynamics with Lagrangian Deep Learningcited by 7
  5. 2020deep21: a Deep Learning Method for 21cm Foreground Removalcited by 6
  6. 2021AI-assisted super-resolution cosmological simulations II: Halo substructures, velocities and higher order statisticscited by 5
  7. 2020Hybrid analytic and machine-learned baryonic property insertion into galactic dark matter haloescited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 6; 2023: 8; 2024: 2; 2025: 0; 2026: 1.

18 papers.

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  1. 2022Learning cosmology and clustering with cosmic graphscited by 16
  2. 2021Inferring halo masses with Graph Neural Networkscited by 13
  3. 2023Robust Field-level Likelihood-free Inference with Galaxiescited by 11
  4. 2022Can denoising diffusion probabilistic models generate realistic astrophysical fields?cited by 10
  5. 2022The CAMELS project: public data releasecited by 6
  6. 2022Augmenting astrophysical scaling relations with machine learning: application to reducing the Sunyaev-Zeldovich flux-mass scattercited by 6
  7. 2022Machine Learning and Cosmologycited by 6
  8. 2023Domain Adaptive Graph Neural Networks for Constraining Cosmological Parameters Across Multiple Data Setscited by 6
  9. 2022Robust field-level inference with dark matter haloscited by 5
  10. 2023The CAMELS project: Expanding the galaxy formation model space with new ASTRID and 28-parameter TNG and SIMBA suitescited by 5
  11. 2024Tackling the Problem of Distributional Shifts: Correcting Misspecified, High-Dimensional Data-Driven Priors for Inverse Problemscited by 5
  12. 2023Field-level simulation-based inference with galaxy catalogs: the impact of systematic effectscited by 4
6 more
  1. 2023Cosmological Field Emulation and Parameter Inference with Diffusion Modelscited by 3
  2. 2024Diffusion-HMC: Parameter Inference with Diffusion-model-driven Hamiltonian Monte Carlocited by 3
  3. 2023Probabilistic reconstruction of Dark Matter fields from biased tracers using diffusion modelscited by 1
  4. 2026Mapping Dark-Matter Clusters via Physics-Guided Diffusion Modelscited by 1
  5. 2023Learnable wavelet neural networks for cosmological inferencecited by 0
  6. 2023Towards out-of-distribution generalization in large-scale astronomical surveys: robust networks learn similar representationscited by 0