Please use this identifier to cite or link to this item: http://hdl.handle.net/2381/44547
Title: Reliable mass calculation in spherical gravitating systems
Authors: Diakogiannis, FI
Lewis, GF
Ibata, RA
Guglielmo, M
Wilkinson, MI
Power, C
First Published: 29-Oct-2018
Publisher: Oxford University Press (OUP), Royal Astronomical Society
Citation: Monthly Notices of the Royal Astronomical Society, 2019, 482(3), pp. 3356–3372
Abstract: 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.
DOI Link: 10.1093/mnras/sty2931
ISSN: 0035-8711
eISSN: 1365-2966
Links: https://academic.oup.com/mnras/article/482/3/3356/5146470
http://hdl.handle.net/2381/44547
Version: Publisher Version
Status: Peer-reviewed
Type: Journal Article
Rights: Copyright © 2018, Oxford University Press (OUP), Royal Astronomical Society. Deposited with reference to the publisher’s open access archiving policy. (http://www.rioxx.net/licenses/all-rights-reserved)
Appears in Collections:Published Articles, Dept. of Physics and Astronomy

Files in This Item:
File Description SizeFormat 
sty2931.pdfPublished (publisher PDF)4.06 MBAdobe PDFView/Open


Items in LRA are protected by copyright, with all rights reserved, unless otherwise indicated.