The position, velocity, and chemical composition of each star provide clues to the evolutionary history of galaxies. While the Gaia mission has offered invaluable information about the Milky Way, to understand the growth of disk galaxies in the Universe we must look beyond our galaxy.
The Andromeda Galaxy (M31) is ideal for this task thanks to its proximity (making it possible for individual stars to be resolved) and its inclination angle, providing a gateway to external galaxies study. Furthermore, our position outside of this galaxy is ideal for an unbiased view of its dark matter halo.
I am presenting a novel Bayesian action-based dynamical tool that exploits stars harvested in M31. This pipeline aims to recover parameters of the galaxy describing its distribution function and dark matter density profile. This will be used to understand the galaxy’s accretion history and accumulation of dark matter.
As a first test, the pipeline has been applied to the Auriga simulations of M31-like galaxies. The new Bayesian action-based model recovers well the parameters of the potential and of the distribution function of the halos even when fitted with non-phase mixed, accreted stellar data. Furthermore, this allowed testing the equilibrium assumptions of galaxies and the generality with which double-power law distribution functions can be used to fit stellar halo components of galaxies.
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