Learning the Cosmic Web: Graph-based Classification of Simulated Galaxies by their Dark Matter Environments

TL;DR

This paper introduces a graph-based machine learning approach using a graph attention network to classify galaxies' dark matter environments in simulations, achieving high accuracy and offering insights into large-scale cosmic structures.

Contribution

The study develops a novel graph-based classification framework that outperforms previous models in identifying cosmic web environments of galaxies using graph metrics and attention mechanisms.

Findings

GAT+ model achieves 85% accuracy for galaxies with stellar mass >10^9 M_sun.

Graph metrics effectively encode local geometric structure of galaxy distributions.

The approach outperforms graph-agnostic models like multilayer perceptrons and GCNs.

Abstract

We present a novel graph-based machine learning classifier for identifying the dark matter cosmic web environments of galaxies. Large galaxy surveys offer comprehensive statistical views of how galaxy properties are shaped by large-scale structure, but this requires robust classifications of galaxies' cosmic web environments. Using stellar mass-selected IllustrisTNG-300 galaxies, we apply a three-stage, simulation-based framework to link galaxies to the total (mainly dark) underlying matter distribution. Here, we apply the following three steps: First, we assign the positions of simulated galaxies to a void, wall, filament, or cluster environment using the T-Web classification of the underlying matter distribution. Second, we construct a Delaunay triangulation of the galaxy distribution to summarise the local geometric structure with ten graph metrics for each galaxy. Third, we train a graph attention network (GAT) on each galaxy's graph metrics to predict its cosmic web environment. For galaxies with stellar mass $\mathrm{>10^9 M_{\odot}}$, our GAT+ model achieves an accuracy of $85\,\%$, outperforming graph-agnostic multilayer perceptrons and graph convolutional networks. Our results demonstrate that graph-based representations of galaxy positions provide a powerful and physically meaningful way to infer dark matter environments. We plan to apply this simulation-based graph modelling to investigate how the properties of observed galaxies from the Dark Energy Spectroscopic Instrument (DESI) survey are influenced by their dark matter environments.