EMPDF: inferring the Milky Way mass with data-driven distribution function in phase space

Journal Article · 26 February 2025 · Monthly Notices of the Royal Astronomical Society

DOI: https://doi.org/10.1093/mnras/staf358 · OSTI ID:2531131

Li, Zhaozhou  ^[1]; Han, Jiaxin  ^[2]; Wang, Wenting  ^[2]; Qian, Yong-Zhong ^[3]; Li, Qingyang  ^[2]; Jing, Yipeng ^[2]; Li, Ting S.  ^[4]

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1. Shanghai Jiao Tong University (China); The Hebrew University, Jerusalem (Israel); Shanghai Key Laboratory for Particle Physics and Cosmology (China)
2. Shanghai Jiao Tong University (China); Shanghai Key Laboratory for Particle Physics and Cosmology (China); Key Laboratory for Particle Astrophysics and Cosmology (MOE), Shanghai (China)
3. University of Minnesota, Minneapolis, MN (United States)
4. University of Toronto, ON (Canada)

We introduce the emPDF (empirical distribution function), a novel dynamical modelling method that infers the gravitational potential from kinematic tracers with optimal statistical efficiency under the minimal assumption of steady state. emPDF determines the best-fitting potential by maximizing the similarity between instantaneous kinematics and the time-averaged phase-space distribution function (DF), which is empirically constructed from observation upon the theoretical foundation of oPDF (Han et al. 2016). This approach eliminates the need for presumed functional forms of DFs or orbit libraries required by conventional DF- or orbit-based methods. emPDF stands out for its flexibility, efficiency, and capability in handling observational effects, making it preferable to the popular Jeans equation or other minimal assumption methods, especially for the Milky Way (MW) outer halo where tracers often have limited sample size and poor data quality. We apply emPDF to infer the MW mass profile using Gaia DR3 data of satellite galaxies and globular clusters, obtaining enclosed masses of M (,r) = 26±8, 46±8, 90±13⁠, and 149±40 x 10¹⁰M_⊙ at r = 30, 50, 100⁠, and 200 kpc, respectively. These are consistent with the updated constraints from simulation-informed DF fitting (Li et al. 2020). While the simulation-informed DF offers superior precision owing to the additional information extracted from simulations, emPDF is independent of such supplementary knowledge and applicable to general tracer populations. emPDF is currently implemented for tracers with complete 6D kinematics within spherical potentials, but it can potentially be extended to address more general problems.

• Research Organization: University of Minnesota, Minneapolis, MN (United States)
• Sponsoring Organization: National Science Foundation (NSF); USDOE; USDOE Office of Science (SC), Nuclear Physics (NP)
• Grant/Contract Number: FG02-87ER40328
• OSTI ID: 2531131
• Journal Information: Monthly Notices of the Royal Astronomical Society, Journal Name: Monthly Notices of the Royal Astronomical Society Journal Issue: 3 Vol. 538; ISSN 1365-2966; ISSN 0035-8711
• Publisher: Oxford University Press (OUP)Copyright Statement
• Country of Publication: United States
• Language: English