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Reliable mass calculation in spherical gravitating systems

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journal contribution
posted on 21.06.2019, 14:32 by FI Diakogiannis, GF Lewis, RA Ibata, M Guglielmo, MI Wilkinson, C Power
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 degeneracy 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 data sets, with the aid of Generative Adversarial Networks: we leverage the power of artificial intelligence to reconstruct the projected line-of-sight velocity distribution non-parametrically. 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.

Funding

FID, GFL, and CP acknowledge support from Australian Research Council Discovery Project (DP140100198). FID also thanks the University of Western Australia for support through a Research Collaboration Awards (PG12105204). GFL thanks the European Southern Observatory (ESO) for support as a visiting astronomer and for hosting him in Garching where the final stages of the preparation of this publication were undertaken. RAI gratefully acknowledges support from a ‘Programme National Cosmologie et Galaxies’ grant.

History

Citation

Monthly Notices of the Royal Astronomical Society, 2019, 482(3), pp. 3356–3372

Author affiliation

/Organisation/COLLEGE OF SCIENCE AND ENGINEERING/Department of Physics and Astronomy

Version

VoR (Version of Record)

Published in

Monthly Notices of the Royal Astronomical Society

Publisher

Oxford University Press (OUP), Royal Astronomical Society

issn

0035-8711

eissn

1365-2966

Acceptance date

25/10/2018

Copyright date

2018

Available date

21/06/2019

Publisher version

https://academic.oup.com/mnras/article/482/3/3356/5146470

Language

en