Self-consistent modelling of the Milky Way’s nuclear stellar disc

The nuclear stellar disc (NSD) is a flattened high-density stellar structure that dominates the gravitational field of the Milky Way at Galactocentric radius $30\, {\rm pc}\lesssim R\lesssim 300\, {\rm pc}$. We construct axisymmetric self-consistent equilibrium dynamical models of the NSD in which t...

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书目详细资料
Main Authors: Sormani, MC, Sanders, JL, Fritz, TK, Smith, LC, Gerhard, O, Schödel, R, Magorrian, J, Neumayer, N, Nogueras-Lara, F, Feldmeier-Krause, A, Mastrobuono-Battisti, A, Schultheis, M, Shahzamanian, B, Vasiliev, E, Klessen, RS, Lucas, P, Minniti, D
格式: Journal article
语言:English
出版: Oxford University Press 2022
实物特征
总结:The nuclear stellar disc (NSD) is a flattened high-density stellar structure that dominates the gravitational field of the Milky Way at Galactocentric radius $30\, {\rm pc}\lesssim R\lesssim 300\, {\rm pc}$. We construct axisymmetric self-consistent equilibrium dynamical models of the NSD in which the distribution function is an analytic function of the action variables. We fit the models to the normalized kinematic distributions (line-of-sight velocities + VIRAC2 proper motions) of stars in the NSD survey of Fritz et al., taking the foreground contamination due to the Galactic Bar explicitly into account using an N-body model. The posterior marginalized probability distributions give a total mass of $M_{\rm NSD} = 10.5^{+1.1}_{-1.0} \times 10^8 \, \, \rm M_\odot$, roughly exponential radial and vertical scale lengths of $R_{\rm disc} = 88.6^{+9.2}_{-6.9} \, {\rm pc}$ and $H_{\rm disc}=28.4^{+5.5}_{-5.5} \, {\rm pc}$, respectively, and a velocity dispersion $\sigma \simeq 70\, {\rm km\, s^{-1}}$ that decreases with radius. We find that the assumption that the NSD is axisymmetric provides a good representation of the data. We quantify contamination from the Galactic Bar in the sample, which is substantial in most observed fields. Our models provide the full 6D (position + velocity) distribution function of the NSD, which can be used to generate predictions for future surveys. We make the models publicly available as part of the software package agama.