Learning cosmology and clustering with cosmic graphs. (arXiv:2204.13713v1 [astro-ph.CO])

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. We employ Graph Neural Networks (GNNs), architectures
designed to work with irregular and sparse data, like the distribution of
galaxies in the Universe. We first show that GNNs can learn to compute the
power spectrum of galaxy catalogues with a few percent accuracy. We then train
GNNs to perform likelihood-free inference at the galaxy-field level. Our …

arxiv astro clustering cosmology graphs learning