Journal Article

Bayes versus the virial theorem: inferring the potential of a galaxy from a kinematical snapshot

John Magorrian

in Monthly Notices of the Royal Astronomical Society

Published on behalf of The Royal Astronomical Society

Volume 437, issue 3, pages 2230-2248
Published in print January 2014 | ISSN: 0035-8711
Published online November 2013 | e-ISSN: 1365-2966 | DOI: https://dx.doi.org/10.1093/mnras/stt2031
Bayes versus the virial theorem: inferring the potential of a galaxy from a kinematical snapshot

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We present a new framework for estimating a galaxy's gravitational potential, Φ, from its stellar kinematics by adopting a fully non-parametric model for the galaxy's unknown action-space distribution function, [math]. Having an expression for the joint likelihood of Φ and f, the likelihood of Φ is calculated by using a Dirichlet process mixture to represent the prior on f and marginalizing. We demonstrate that modelling machinery constructed using this framework is successful at recovering the potentials of some simple systems from perfect discrete kinematical data, a situation handled effortlessly by traditional moment-based methods, such as the virial theorem, but in which other, more modern, methods are less than satisfactory. We show how to generalize the machinery to account for realistic observational errors and selection functions. A practical implementation is likely to raise some interesting algorithmic and computational challenges.

Keywords: methods: data analysis; galaxies: kinematics and dynamics; galaxies: structure

Journal Article.  15059 words.  Illustrated.

Subjects: Astronomy and Astrophysics

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