Turbulent momentum pinch of diamagnetic flows in a tokamak

Nuclear Fusion IOP Publishing 54:2 (2014) 022002

Authors:

Jungpyo Lee, Felix I Parra, Michael Barnes

The RAVE survey: the Galactic escape speed and the mass of the Milky Way

Astronomy & Astrophysics EDP Sciences 562 (2014) a91

Authors:

T Piffl, C Scannapieco, J Binney, M Steinmetz, R-D Scholz, MEK Williams, RS de Jong, G Kordopatis, G Matijevič, O Bienaymé, J Bland-Hawthorn, C Boeche, K Freeman, B Gibson, G Gilmore, EK Grebel, A Helmi, U Munari, JF Navarro, Q Parker, WA Reid, G Seabroke, F Watson, RFG Wyse, T Zwitter

Detecting gravitational waves from the galactic center with Pulsar Timing

(2014)

Authors:

Alak Ray, Bence Kocsis, Simon Portegies Zwart

New distances to RAVE stars

Monthly Notices of the Royal Astronomical Society Oxford University Press (OUP) 437:1 (2014) 351-370

Authors:

J Binney, B Burnett, G Kordopatis, PJ McMillan, S Sharma, T Zwitter, O Bienaymé, J Bland-Hawthorn, M Steinmetz, G Gilmore, MEK Williams, J Navarro, EK Grebel, A Helmi, Q Parker, WA Reid, G Seabroke, F Watson, RFG Wyse

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

Monthly Notices of the Royal Astronomical Society 437:3 (2014) 2230-2248

Abstract:

I present a new framework for estimating a galaxy's gravitational potential, Phi, from its stellar kinematics. It adopts a fully non-parametric model for the galaxy's unknown phase-space distribution function, f, that takes full advantage of Jeans' theorem. Given an expression for the joint likelihood of Phi and f, the likelihood of Phi is calculated by using a Dirichlet process mixture to represent the prior on f and marginalising. I demonstrate that modelling machinery constructed using this framework is successful at recovering the potentials of some simple systems given perfect kinematical data, a situation handled effortlessly by traditional moment-based methods, such as the virial theorem, but in which the more modern extended-Schwarzschild method fails. Unlike moment-based methods, however, the models constructed using this framework can easily be generalised to take account of realistic observational errors and selection functions.