Mapping Dark Matter in the Milky Way using Normalizing Flows and Gaia DR3

15 Jun 2023, 16:30
20m
Auditorium (IBS Science and Culture Center)

Auditorium

IBS Science and Culture Center

Dark Matter Physics Parallel: Dark Matter 6

Speaker

Sung Hak Lim (Rutgers University)

Description

We present a novel, data-driven analysis of Galactic dynamics, using unsupervised machine learning -- in the form of density estimation with normalizing flows -- to learn the underlying phase space distribution of 6 million nearby stars from the Gaia DR3 catalog. Solving the collisionless Boltzmann equation with the assumption of approximate equilibrium, we calculate -- for the first time ever -- a model-free, unbinned, fully 3D map of the local acceleration and mass density fields within a 3 kpc sphere around the Sun. As our approach makes no assumptions about symmetries, we can test for signs of disequilibrium in our results. We find our results are consistent with equilibrium at the 10% level, limited by the current precision of the normalizing flows. After subtracting the known contribution of stars and gas from the calculated mass density, we find clear evidence for dark matter throughout the analyzed volume. Assuming spherical symmetry and averaging mass density measurements, we find a local dark matter density of 0.47±0.05GeV/cm3. We fit our results to a generalized NFW, and find a profile broadly consistent with other recent analyses.

Primary authors

Sung Hak Lim (Rutgers University) Eric Putney (Rutgers University) Matthew R. Buckley (Rutgers University) David Shih (Rutgers University)

Presentation materials