Theoretical and Natural Science

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Theoretical and Natural Science

Vol. 13, 30 November 2023

Open Access | Article

Deciphering dark matter: A computational analysis of stellar velocity distributions

Mingfei Xu * 1
1 Stevens Institute of Technology

* Author to whom correspondence should be addressed.

Advances in Humanities Research, Vol. 13, 1-10
Published 30 November 2023. © 2023 The Author(s). Published by EWA Publishing
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Citation Mingfei Xu. Deciphering dark matter: A computational analysis of stellar velocity distributions. TNS (2023) Vol. 13: 1-10. DOI: 10.54254/2753-8818/13/20240717.


This study examines the dark matter density in the Galaxy by fitting recent data on stellar rotational velocities. We employ a model including the disk, the bulge, and the dark matter halo. The disk’s density is parameterized using a modified Bessel function, and the bulge is modeled using both the De Vaucouleurs and Exponential Sphere functions to better fit large and small radii respectively. The dark halo is modeled using the generalized Navarro-Frenk-White profile, with slope parameters for standard NFW and Moore profiles, and an Isothermal profile for comparison. The fit of the model parameters is established using local Dark Matter density and total Dark Matter mass as boundary conditions, with circular velocities derived via a Newtonian approach. The least chi-square (χ 2 ) method is used for rotation curve fitting. Results demonstrate successful fitting of rotational velocity data and significant influence of the DH at large radial distances from the Galactic center.


Dark Matter, Dark Halo, Rotation Curve Fitting, Milky Way


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Data Availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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Volume Title
Proceedings of the 3rd International Conference on Computing Innovation and Applied Physics
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30 November 2023
Theoretical and Natural Science
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© 2023 The Author(s)
Open Access
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Copyright © 2023 EWA Publishing. Unless Otherwise Stated