Work

Cosmology from Point Clouds with Dark Matter Halos from the Quijote Simulations

Year: 2025

Type: article

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.

Source: The Astrophysical Journal

DOI: 10.3847/1538-4357/adc99d

Authors: Atrideb Chatterjee, Francisco Villaescusa-Navarro

Institutions: University of Groningen, Inter-University Centre for Astronomy and Astrophysics, Princeton University, Flatiron Institute

Language: English

Topic: Galaxies: Formation, Evolution, Phenomena

Subfield: Astronomy and Astrophysics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $4,499

Funder: Flatiron Health