Reliable mass calculation in spherical gravitating systems

We present an innovative approach to the methodology of dynamical modelling, allowing practical reconstruction of the underlying dark matter mass without assuming both the density and anisotropy functions. With this, the mass-anisotropy degener-acy is reduced to simple model inference, incorporating...

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Bibliographic Details
Published in:Monthly notices of the Royal Astronomical Society 2019-01, Vol.482 (3), p.3356-3372
Main Authors: Diakogiannis, Foivos I, Lewis, Geraint F, Ibata, Rodrigo A, Guglielmo, Magda, Wilkinson, Mark I, Power, Chris
Format: Article
Language:English
Subjects:
Astrophysics
Galactic Astrophysics
Physics
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Summary:We present an innovative approach to the methodology of dynamical modelling, allowing practical reconstruction of the underlying dark matter mass without assuming both the density and anisotropy functions. With this, the mass-anisotropy degener-acy is reduced to simple model inference, incorporating the uncertainties inherent with observational data, statistically circumventing the mass-anisotropy degeneracy in spherical collisionless systems. We also tackle the inadequacy that the Jeans method of moments has on small datasets, with the aid of Generative Adversarial Networks: we leverage the power of artificial intelligence to reconstruct non-parametrically the projected line-of-sight velocity distribution. We show with realistic numerical simulations of dwarf spheroidal galaxies that we can distinguish between competing dark matter distributions and recover the anisotropy and mass profile of the system.
ISSN:0035-8711
1365-2966
DOI:10.1093/mnras/sty2931