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Found 9 papers, 6 papers with code
Robust Field-level Likelihood-free Inference with Galaxies
We train graph neural networks to perform field-level likelihood-free inference using galaxy catalogs from state-of-the-art hydrodynamic simulations of the CAMELS project.
Robust field-level inference with dark matter halos
More importantly, we find that our models are very robust: they can infer the value of $\Omega_{\rm m}$ and $\sigma_8$ when tested using halo catalogues from thousands of N-body simulations run with five different N-body codes: Abacus, CUBEP$^3$M, Enzo, PKDGrav3, and Ramses.
Learning cosmology and clustering with cosmic graphs
We train deep learning models on thousands of galaxy catalogues from the state-of-the-art hydrodynamic simulations of the CAMELS project to perform regression and inference.
The CAMELS project: public data release
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.
Weighing the Milky Way and Andromeda with Artificial Intelligence
We present new constraints on the masses of the halos hosting the Milky Way and Andromeda galaxies derived using graph neural networks.
Inferring halo masses with Graph Neural Networks
Furthermore, a GNN trained on a suite of simulations is able to preserve part of its accuracy when tested on simulations run with a different code that utilizes a distinct subgrid physics model, showing the robustness of our method.
Ranked #1 on
Graph Learning
on CAMELS
The CAMELS Multifield Dataset: Learning the Universe's Fundamental Parameters with Artificial Intelligence
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.
Removing Astrophysics in 21 cm maps with Neural Networks
From these simulations we produce tens of thousands of 21 cm maps at redshifts $10\leq z\leq 20$.
Cosmology and Nongalactic Astrophysics Astrophysics of Galaxies
On the maximum amplitude of the high-redshift 21-cm absorption feature
We examine the maximum possible strength of the global 21-cm absorption dip on the Cosmic Background Radiation at high-redshift caused by the atomic intergalactic medium, when the Lyman-$\alpha$ coupling is maximum, assuming no exotic cooling mechanisms from interactions with dark matter.
Cosmology and Nongalactic Astrophysics
