CAMELS Project Releases Expanded Cosmological Simulations with 35 Varying Parameters
Researchers have created 1,192 new cosmological simulations as part of the CAMELS project, varying 35 parameters of the IllustrisTNG model in larger (50Mpc/h)³ volumes—eight times bigger than previous versions. The larger simulations enable better parameter inference using machine learning techniques like neural networks and convolutional networks. This work advances the ability to extract cosmological and astrophysical information from simulated universe data, with results publicly released for the research community.
The CAMELS collaboration has released a new suite of 1,192 cosmological simulations that systematically explore variations in 35 cosmological, astrophysical, and numerical parameters around the IllustrisTNG model baseline. Each simulation occupies a volume eight times larger than previous CAMELS simulations, reducing sample variance and providing access to more massive structures and diverse cosmic environments. The team trained multiple machine learning architectures—multilayer perceptrons, convolutional neural networks, graph neural networks, and Gaussian processes—on different data types including matter power spectra, projected maps, galaxy distributions, and halo properties to infer simulation parameters. While the larger volumes generally produced tighter constraints on parameters compared to earlier (25Mpc/h)³ simulations, the improvements scaled more weakly than expected from the increased data volume, suggesting either information loss from mode coupling or complex degeneracies in parameter space. The researchers also examined how four new parameters controlling ionizing background radiation affect intergalactic medium temperature statistics. All simulation outputs and supporting data have been made publicly available through the CAMELS project documentation.
What different sources said
- arXiv astro-phCenter
Learning the Universe with the 2nd Generation of CAMELS: Varying 35 parameters of the IllustrisTNG model in (50Mpc/h)^3 boxes
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