Models of rotating coronae. Issue 3 (14th September 2018)

• Record Type: Journal Article
• Authors: Sormani, Mattia C; Sobacchi, Emanuele; Pezzulli, Gabriele; Binney, James; Klessen, Ralf S
• Is Part Of: Monthly notices of the Royal Astronomical Society. Volume 481:Issue 3(2018)
• Page Start: 3370
• Page End: 3381
• Publication Date: 2018-09-14
• Subjects: Galaxy: halo -- galaxies: evolution -- galaxies: haloes -- intergalactic medium; Astronomy -- Periodicals; Periodicals; 520.5
• DOI: 10.1093/mnras/sty2500
• Languages: English
• ISSNs: 0035-8711

Abstract

ABSTRACT: Fitting equilibrium dynamical models to observational data is an essential step in understanding the structure of the gaseous hot haloes that surround our own and other galaxies. However, the two main categories of models that are used in the literature are poorly suited for this task: (i) simple barotropic models are analytic and can therefore be adjusted to match the observations, but are clearly unrealistic because the rotational velocity $v$ ϕ ( R, $z$ ) does not depend on the distance $z$ from the galactic plane, while (ii) models obtained as a result of cosmological galaxy formation simulations are more realistic, but are impractical to fit to observations due to high computational cost. Here we bridge this gap by presenting a general method to construct axisymmetric baroclinic equilibrium models of rotating galactic coronae in arbitrary external potentials. We consider in particular a family of models whose equipressure surfaces in the ( R, $z$ ) plane are ellipses of varying axis ratio. These models are defined by two one-dimensional functions, the axial ratio of pressure q axis ($z$ ) and the value of the pressure P axis ($z$ ) along the galaxy's symmetry axis. These models can have a rotation speed $v$ ϕ ( R, $z$ ) that realistically decreases as one moves away from the galactic plane, and can reproduce the angular momentum distribution found in cosmological simulations. The models are computationally cheap to construct and can thus be used in fitting algorithms. We provide a python code that given q axis ($z$ ), P axis ($z$ ), and Φ( R, $z$ ) returns ρ( R, $z$ ), T ( R, $z$ ), P ( R, $z$ ), $v$ ϕ ( R, $z$ ). We show a few examples of these models using the Milky Way as a case study.