Mapping dark matter in the Milky Way using normalizing flows and Gaia DR3

被引：7

作者：

Lim, Sung Hak ^{[1
,2
]}

Putney, Eric ^{[2
]}

Buckley, Matthew R. ^{[2
]}

Shih, David ^{[2
]}

机构：

[1] Inst Basic Sci IBS, Ctr Theoret Phys Universe, Particle Theory & Cosmol Grp, 55 Expo Ro, Daejeon 34126, South Korea

[2] Rutgers State Univ, Dept Phys & Astron, NHETC, 126 Frelinghuysen Rd, Piscataway, NJ 08854 USA

来源：

JOURNAL OF COSMOLOGY AND ASTROPARTICLE PHYSICS | 2025年 / 01期

基金：

美国国家科学基金会;

关键词：

Machine learning; rotation curves of galaxies; galaxy dynamics; SOLAR NEIGHBORHOOD; DENSITY; MASS; DISTANCES; PROFILE; HALO; KPC;

D O I：

10.1088/1475-7516/2025/01/021

中图分类号：

P1 [天文学];

学科分类号：

0704 ;

摘要：

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 equilibrium collisionless Boltzmann equation, we calculate for the first time ever a model-free, unbinned estimate 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 +/- 0.05 GeV/cm3. We compute the dark matter density at four radii in the stellar halo and fit to a generalized NFW profile. Although the uncertainties are large, we find a profile broadly consistent with recent analyses.

引用

页数：35

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