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We present a novel, data-driven analysis of Galactic dynamics, usingunsupervised machine learning -- in the form of density estimation withnormalizing flows -- to learn the underlying phase space distribution of 6million nearby stars from the Gaia DR3 catalog. Solving the collisionlessBoltzmann equation with the assumption of approximate equilibrium, we calculate-- for the first time ever -- a model-free, unbinned, fully 3D map of the localacceleration and mass density fields within a 3 kpc sphere around the Sun. Asour approach makes no assumptions about symmetries, we can test for signs ofdisequilibrium in our results. We find our results are consistent withequilibrium at the 10% level, limited by the current precision of thenormalizing flows. After subtracting the known contribution of stars and gasfrom the calculated mass density, we find clear evidence for dark matterthroughout the analyzed volume. Assuming spherical symmetry and averaging massdensity measurements, we find a local dark matter density of $0.47\pm0.05\;\mathrm{GeV/cm}^3$. We fit our results to a generalized NFW, and find aprofile broadly consistent with other recent analyses.

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