Weighing the Milky Way and Andromeda with Artificial Intelligence
Pablo Villanueva-Domingo, Francisco Villaescusa-Navarro, Shy Genel,, Daniel Angl\'es-Alc\'azar, Lars Hernquist, Federico Marinacci, David N., Spergel, Mark Vogelsberger, Desika Narayanan

TL;DR
This paper uses graph neural networks trained on hydrodynamic simulations to estimate the masses of the Milky Way and Andromeda halos, effectively handling uncertainties and aligning with traditional estimates.
Contribution
It introduces a novel AI-based approach employing graph neural networks trained on simulations for halo mass inference, incorporating uncertainties.
Findings
Constraints agree with traditional methods
Effective likelihood-free inference achieved
Models trained on CAMELS simulations
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.
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Taxonomy
TopicsGalaxies: Formation, Evolution, Phenomena · Data Visualization and Analytics · Gaussian Processes and Bayesian Inference
