Topological neural networks using tetrahedra, clusters and hyperedges built from halo catalogs lower inference error on Omega_m by 22% and on sigma_8 by up to 60% versus graph neural networks on Quijote.
Cosmology from Point Clouds with Dark Matter Halos from the Quijote Simulations
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abstract
We train a novel deep learning architecture to perform likelihood-free inference on the value of the cosmological parameters from halo catalogs of the Quijote N-body simulations. Our model takes as input a halo catalog where each halo is characterized by its position, mass, and velocity modulus. By construction, our model is E(3) invariant and is designed to extract information hierarchically. Unlike graph neural networks, it does not require the transformation of the input halo (or galaxy) catalog into a graph. Given its simplicity, our model can process point clouds with large numbers of points. We discuss the advantages of this class of methods but also point out their limitations and potential ways to improve them for cosmological data.
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astro-ph.CO 1years
2025 1verdicts
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Cosmology with Topological Deep Learning
Topological neural networks using tetrahedra, clusters and hyperedges built from halo catalogs lower inference error on Omega_m by 22% and on sigma_8 by up to 60% versus graph neural networks on Quijote.