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PI:  John F. Wu (email, website)
Group Website: Chesapeake ML-Astro, ISM*@ST
Project Duration: 6 month rotation project

Background and Context

Galaxies grow and evolve in dark matter halos amidst a complex, large-scale environment. These galaxies (or dark matter subhalos) can be represented as nodes on a graph, and their relationships or effective interactions can be represented as edges on a graph. By train graph neural networks (GNNs), we can learn the physical relationships between galaxies, subhalos, and their surroundings directly from large data sets, such as hydrodynamic simulations (see Figure 1 below; Wu & Jespersen 2023). This has implications for modeling the galaxy-halo connection (e.g., Wechsler & Tinker 2018) and large-scale correlations of galaxy properties (e.g., Hearin et al. 2016).

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