Breaking beta: a comparison of mass modelling methods for spherical systems

Author:

Read J I1ORCID,Mamon G A2ORCID,Vasiliev E345ORCID,Watkins L L678ORCID,Walker M G9,Peñarrubia J10,Wilkinson M11,Dehnen W1112ORCID,Das P14ORCID

Affiliation:

1. Department of Physics, University of Surrey, Guildford GU2 7XH, UK

2. Institut d’Astrophysique de Paris (UMR 7095: CNRS & Sorbonne Université), F-75014 Paris, France

3. Institute of Astronomy, University of Cambridge, Madingley Road, Cambridge CB3 0HA, UK

4. Rudolf Peierls Centre for Theoretical Physics, University of Oxford, Oxford OX1 3NP, UK

5. Lebedev Physical Institute, Leninsky Prospekt 53, Moscow 119991, Russia

6. AURA for the European Space Agency (ESA), ESA Office, Space Telescope Science Institute, 3700 San Martin Drive, Baltimore, MD 21218, USA

7. European Southern Observatory, Karl-Schwarzschild-Straße 2, D-85748 Garching bei München, Germany

8. Department of Astrophysics, University of Vienna, Türkenschanzstraße 17, A-1180 Vienna, Austria

9. McWilliams Center for Cosmology, Department of Physics, Carnegie Mellon University, Pittsburgh, PA 15213, USA

10. Institute for Astronomy, University of Edinburgh, Blackford Hill, Edinburgh EH9 3HJ, UK

11. Department of Physics & Astronomy, University of Leicester, Leicester LE1 7RH, UK

12. Universitäts-Sternwarte der Ludwig-Maximilians-Universität, Scheinerstrasse 1, D-81679 München, Germany

Abstract

ABSTRACT We apply four different mass modelling methods to a suite of publicly available mock data for spherical stellar systems. We focus on the recovery of the density and velocity anisotropy as a function of radius, either using line-of-sight velocity data only or adding proper motion data. All methods perform well on isotropic and tangentially anisotropic mock data, recovering the density and velocity anisotropy within their 95 per cent confidence intervals over the radial range 0.25 < R/R1/2 < 4, where R1/2 is the half-light radius. However, radially anisotropic mocks are more challenging. For line-of-sight data alone, only methods that use information about the shape of the velocity distribution function are able to break the degeneracy between the density profile and the velocity anisotropy, β, to obtain an unbiased estimate of both. This shape information can be obtained through directly fitting a global phase-space distribution function, by using higher order ‘virial shape parameters’ or by assuming a Gaussian velocity distribution function locally, but projecting it self-consistently along the line of sight. Including proper motion data yields further improvements, and in this case, all methods give a good recovery of both the radial density and velocity anisotropy profiles.

Funder

Science and Technology Facilities Council

College of Natural Resources and Sciences, Humboldt State University

Nippon Life Insurance Foundation

H2020 European Research Council

National Aeronautics and Space Administration

Publisher

Oxford University Press (OUP)

Subject

Space and Planetary Science,Astronomy and Astrophysics

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