Weighing the Milky Way and Andromeda with Artificial Intelligence

Abstract

We present new constraints on the masses of the halos hosting the Milky Way and Andromeda galaxies derived using graph neural networks. Our models, trained on thousands of state-of-the-art hydrodynamic simulations of the CAMELS project, only make use of the positions, velocities and stellar masses of the galaxies belonging to the halos, and are able to perform likelihood-free inference on halo masses while accounting for both cosmological and astrophysical uncertainties. Our constraints are in agreement with estimates from other traditional methods.

2 figures, 2 tables, 7 pages. Code publicly available at https://github.com/PabloVD/HaloGraphNet

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• Fast Graph Representation Learning with PyTorch Geometric
• Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges
• The CAMELS Multifield Dataset: Learning the Universe's Fundamental Parameters with Artificial Intelligence
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• Inferring halo masses with Graph Neural Networks

Cited by in corpus (1)

• Inferring halo masses with Graph Neural Networks