Speaker
Description
Accurate constraint on the total mass of the Milky Way (MW) is crucial yet remains significantly uncertain after decades of efforts.
In this study, we employ a novel data-driven dynamical modeling method, the empirical distribution function (\empdf), to refine estimates of the Milky Way mass profile using a large sample of halo stars from the Dark Energy Spectroscopic Instrument Milky Way Survey (DESI MWS). We apply the model to mock data from AuriDESI first. Our model can ensemble unbiasedly recover the mass profile with error-free mock data in the ideal case, but is subject to significant biases when observational errors are included. We thus use the amount of bias in best-fit halo model parameters based on the mock data to calibrate the observational constraints, and we also calibrate our uncertainties to include the systematic source of errors according to the mock tests. After the calibration, we derive an enclosed mass profile consistent with previous studies, with best constrained virial mass and concentration of $\log M_{200c}=12.07_{-0.13}^{+0.17}(_{-0.21}^{+0.24})$ and $\log c_{200c} = 1.16_{-0.14}^{+0.11}(_{-0.26}^{+0.24})$ for BHBs and $\log M_{200c}=12.10_{-0.10}^{+0.10}(_{-0.15}^{+0.14})$ and $\log c_{200c} = 1.24_{-0.09}^{+0.06}(_{-0.18}^{+0.16})$ for RR Lyrae stars. This work demonstrates the ability to combine abundant observed datasets with minimal assumption method to constrain the mass of MW, providing a building block for the precise measurement of MW mass using the bulk of the data.
