# Estimating Dark Matter Halo Masses in Simulated Galaxy Clusters with Graph Neural Networks

arXiv: https://arxiv.org/abs/2411.12629 · HTML: https://mlsanity.com/paper/2411.12629/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 39 references that are in the map.
- First posted on arXiv 19 Nov 2024.

## Builds on (most cited first, 7 of 7)

- [Gaussian Error Linear Units (GELUs)](https://mlsanity.com/paper/1606.08415/) (2016)
- [Fast Graph Representation Learning with PyTorch Geometric](https://mlsanity.com/paper/1903.02428/) (2019)
- [Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks](https://mlsanity.com/paper/1905.07953/) (2019)
- [Return of Frustratingly Easy Domain Adaptation](https://mlsanity.com/paper/1511.05547/) (2015)
- [Discovering Symbolic Models from Deep Learning with Inductive Biases](https://mlsanity.com/paper/2006.11287/) (2020)
- [An introduction to domain adaptation and transfer learning](https://mlsanity.com/paper/1812.11806/) (2018)
- [DeepMerge II: Building Robust Deep Learning Algorithms for Merging Galaxy Identification Across Domains](https://mlsanity.com/paper/2103.01373/) (2021)

## Built on by (most cited first, 1 of 1)

- [Set-based Implicit Likelihood Inference of Galaxy Cluster Mass](https://mlsanity.com/paper/2507.20378/) (2025)

## Abstract (arXiv)

Galaxies grow and evolve in dark matter halos. Because dark matter is not visible, galaxies' halo masses (rmM_rmhalo) must be inferred indirectly. We present a graph neural network (GNN) model for predicting rmM_rmhalo from stellar mass (rmM*) in simulated galaxy clusters using data from the IllustrisTNG simulation suite. Unlike traditional machine learning models like random forests, our GNN captures the information-rich substructure of galaxy clusters by using spatial and kinematic relationships between galaxy neighbour. A GNN model trained on the TNG-Cluster dataset and independently tested on the TNG300 simulation achieves superior predictive performance compared to other baseline models we tested. Future work will extend this approach to different simulations and real observational datasets to further validate the GNN model's ability to generalise.