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

TL;DR

This paper introduces a novel deep learning model for likelihood-free inference of cosmological parameters from halo catalogs, leveraging E(3) invariance and hierarchical feature extraction without graph transformations.

Contribution

The paper presents a new deep learning architecture that processes point cloud data directly for cosmological inference, avoiding graph conversion and handling large datasets efficiently.

Findings

Model is E(3) invariant and hierarchical.

Capable of processing large point clouds.

Discusses advantages and limitations of the approach.

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.