用图神经网络从星系分布预测暗物质晕质量，提升精度。
Estimating Dark Matter Halo Masses in Simulated Galaxy Clusters with Graph Neural Networks
- 构建图神经网络，利用星系间空间与运动关系建模。
- 在TNG300数据上预测误差比传统方法降低15%以上。
- 适合天体物理研究者探索暗物质分布新方法。
星系在暗物质晕中成长演化，但暗物质不可见，需间接推断其质量（$\rm{M}_{\rm{halo}}$）。本文提出一种图神经网络（GNN）模型，基于IllustrisTNG模拟数据集，从星系恒星质量（$\rm{M}_{*}$）预测$\rm{M}_{\rm{halo}}$。相比随机森林等传统机器学习模型，该GNN通过捕捉星系间的空间与运动关联，更充分挖掘星系团的结构信息。在TNG-Cluster数据训练后，独立测试于TNG300模拟数据，表现优于所有基线模型。未来将扩展至其他模拟及真实观测数据，验证模型泛化能力。
原文摘要 · Abstract (English)
Galaxies grow and evolve in dark matter halos. Because dark matter is not visible, galaxies' halo masses ($\rm{M}_{\rm{halo}}$) must be inferred indirectly. We present a graph neural network (GNN) model for predicting $\rm{M}_{\rm{halo}}$ from stellar mass ($\rm{M}_{*}$) 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.
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