Uncover 3D Dark Matter Distribution of the Milky Way by an Empirical Triaxial Orbit-Superposition Model: Method Validation
Ling Zhu, Xiang-Xiang Xue, Shude Mao, Chengqun Yang, Lan Zhang

TL;DR
This paper presents a new empirical triaxial orbit-superposition model to accurately map the 3D dark matter distribution of the Milky Way halo, validated with mock datasets mimicking real observations.
Contribution
It introduces a minimally assumption-based dynamical model that effectively constrains the shape and density of the Milky Way's dark matter halo using orbit superposition techniques.
Findings
Successfully recovers 3D dark matter density in mock galaxies
Accurately constrains halo shape and radial density distribution
Validates method with realistic mock observational data
Abstract
We introduce a novel dynamical model, named empirical triaxial orbit-superposition model, for the Milky Way halo. This model relies on minimal physical assumptions that the system is stationary, meaning the distribution function in 6D phase-space does not change when the stars orbiting in the correct gravitational potential. We validate our method by applying it to mock datasets that mimic the observations of the Milky Way halo from LAMOST + Gaia with stars' 3D position and 3D velocity observed. By removing the stellar disk and substructures, correcting the selection function, we obtain a sample of smooth halo stars considered as stationary and complete. We construct a gravitational potential including a highly flexible triaxial dark matter halo with adaptable parameters. Within each specified gravitational potential, we integrate orbits of these halo stars, and build a model by…
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