Geometric deep learning for galaxy-halo connection: a case study for galaxy intrinsic alignments

TL;DR

This paper introduces a geometric deep learning approach using E(3) equivariant graph neural networks to simulate galaxy intrinsic alignments, capturing complex orientations and scalar features efficiently for large-scale cosmological surveys.

Contribution

It presents a novel deep generative model that accurately reproduces galaxy orientations and scalar features, respecting Euclidean symmetries, for use in large cosmological simulations.

Findings

Model accurately reproduces galaxy orientations from simulations.

Jointly models Euclidean scalars and SO(3) orientations.

Demonstrates scalability for large cosmological datasets.

Abstract

Forthcoming cosmological imaging surveys, such as the Rubin Observatory LSST, require large-scale simulations encompassing realistic galaxy populations for a variety of scientific applications. Of particular concern is the phenomenon of intrinsic alignments (IA), whereby galaxies orient themselves towards overdensities, potentially introducing significant systematic biases in weak gravitational lensing analyses if they are not properly modeled. Due to computational constraints, simulating the intricate details of galaxy formation and evolution relevant to IA across vast volumes is impractical. As an alternative, we propose a Deep Generative Model trained on the IllustrisTNG-100 simulation to sample 3D galaxy shapes and orientations to accurately reproduce intrinsic alignments along with correlated scalar features. We model the cosmic web as a set of graphs, each graph representing a halo with nodes representing the subhalos/galaxies. The architecture consists of a SO(3) $\times$ $\mathbb{R}^n$ diffusion generative model, for galaxy orientations and $n$ scalars, implemented with E(3) equivariant Graph Neural Networks that explicitly respect the Euclidean symmetries of our Universe. The model is able to learn and predict features such as galaxy orientations that are statistically consistent with the reference simulation. Notably, our model demonstrates the ability to jointly model Euclidean-valued scalars (galaxy sizes, shapes, and colors) along with non-Euclidean valued SO(3) quantities (galaxy orientations) that are governed by highly complex galactic physics at non-linear scales.