Kavli Affiliate: Shigeki Matsumoto
| First 5 Authors: Sung Hak Lim, Kohei Hayashi, Shun’ichi Horigome, Shigeki Matsumoto, Mihoko M. Nojiri
| Summary:
The kinematics of stars in dwarf spheroidal galaxies have been studied to
understand the structure of dark matter halos. However, the kinematic
information of these stars is often limited to celestial positions and
line-of-sight velocities, making full phase space analysis challenging.
Conventional methods rely on projected analytic phase space density models with
several parameters and infer dark matter halo structures by solving the
spherical Jeans equation. In this paper, we introduce an unsupervised machine
learning method for solving the spherical Jeans equation in a model-independent
way as a first step toward model-independent analysis of dwarf spheroidal
galaxies. Using equivariant continuous normalizing flows, we demonstrate that
spherically symmetric stellar phase space densities and velocity dispersions
can be estimated without model assumptions. As a proof of concept, we apply our
method to Gaia challenge datasets for spherical models and measure dark matter
mass densities given velocity anisotropy profiles. Our method can identify halo
structures accurately, even with a small number of tracer stars.
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