Weighing the Milky Way and Andromeda with Artificial Intelligence
Astrophysics of Galaxies2021-12-01v1Cosmology and Nongalactic AstrophysicsInstrumentation and Methods for AstrophysicsArtificial IntelligenceMachine Learning
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.
@article{arxiv.2111.14874,
title = {Weighing the Milky Way and Andromeda with Artificial Intelligence},
author = {Pablo Villanueva-Domingo and Francisco Villaescusa-Navarro and Shy Genel and Daniel Anglés-Alcázar and Lars Hernquist and Federico Marinacci and David N. Spergel and Mark Vogelsberger and Desika Narayanan},
journal= {arXiv preprint arXiv:2111.14874},
year = {2021}
}
Comments
2 figures, 2 tables, 7 pages. Code publicly available at https://github.com/PabloVD/HaloGraphNet