Weighing the Milky Way and Andromeda with Artificial Intelligence
This work provides incremental improvements in astrophysical mass estimation for galaxies, aiding astronomers in understanding dark matter and galaxy formation.
The researchers tackled the problem of estimating the masses of the Milky Way and Andromeda galaxy halos by using graph neural networks trained on hydrodynamic simulations, achieving constraints that align with traditional methods.
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.