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

TL;DR

The CAMELS Multifield Dataset (CMD) is a large, detailed collection of simulated cosmic maps and grids designed to train machine learning models for understanding the universe's fundamental parameters.

Contribution

We introduce the largest multifield cosmological dataset for machine learning, enabling new approaches to parameter inference from simulated cosmic structures.

Findings

Dataset contains over 70 Terabytes of data from 2,000 simulations.

Demonstrated potential for machine learning in cosmological parameter inference.

Provided a community challenge to advance the field.

Abstract

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 $\sim$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.