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arXiv 1405.7435astro-ph.GA
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使用RAVE和GCS恒星巡天对Milky Way进行运动学建模

Kinematic modelling of the Milky Way using the RAVE and GCS stellar surveys

S. Sharma, J. Bland-Hawthorn, J. Binney, K. C. Freeman, M. Steinmetz, C. Boeche, O. Bienayme, B. K. Gibson, G. F. Gilmore, E. K. Grebel, A. Helmi, G. Kordopatis… 展开作者

S. Sharma, J. Bland-Hawthorn, J. Binney, K. C. Freeman, M. Steinmetz, C. Boeche, O. Bienayme, B. K. Gibson, G. F. Gilmore, E. K. Grebel, A. Helmi, G. Kordopatis, U. Munari, J. F. Navarro, Q. A. Parker, W. A. Reid, G. M. Seabroke, A. Siebert, F. Watson, M. E. K. Williams, R. F. G. Wyse, T. Zwitter

摘要:

我们利用RAVE和GCS巡天资料研究了银河系盘的运动学参数。我们这样做是通过拟合的运动学模型的数据,考虑到数据的选择功能。对于GCS中的恒星,我们使用所有的相空间坐标,但对于RAVE恒星,我们只使用$(l,b,v_{\rm los})$。使用MCMC技术,我们研究了给定数据的参数的全后验分布。我们调查的“年龄-速度色散”关系的三个运动学分量($σ_R,σ_ϕ,σ_z$),径向依赖的速度色散,太阳特有的运动($U_{\odot},V_{\odot}, W_{\odot} $),在太阳的圆周速度$Θ_0$和下降的平均方位角运动与高度以上的中平面。我们证实,贝桑松式高斯模型准确地适合GCS数据,但未能匹配的空间扩展的RAVE调查的细节。特别是,舒分布函数(DF)处理非圆形轨道更准确,并提供了一个更好的适合的运动学数据。高斯分布函数不仅拟合数据差,但系统地低估了下降的速度分散半径。我们发现,一些参数之间存在的相关性,这突出了做关节配合的重要性。大尺寸的RAVE调查,使我们能够获得大多数参数的精确值。然而,仍然存在较大的系统不确定性,特别是在$V_{\odot}$和$Θ_0$中。我们发现,对于一个扩展的样本的恒星,$Θ_0$被低估了多达$10\%$,如果被忽略的平均方位角运动的垂直依赖。使用一个简单的模型,垂直依赖的运动学,我们发现,它是可能的,以匹配的Sgr A* 自行车,而不需要任何$V_{\odot}$是大于本地估计的调查,如GCS。

英文摘要:

We investigate the kinematic parameters of the Milky Way disc using the RAVE and GCS stellar surveys. We do this by fitting a kinematic model to the data taking the selection function of the data into account. For stars in the GCS we use all phase-space coordinates, but for RAVE stars we use only $(l,b,v_{\rm los})$. Using MCMC technique, we investigate the full posterior distributions of the parameters given the data. We investigate the `age-velocity dispersion' relation for the three kinematic components ($σ_R,σ_ϕ,σ_z$), the radial dependence of the velocity dispersions, the Solar peculiar motion ($U_{\odot},V_{\odot}, W_{\odot} $), the circular speed $Θ_0$ at the Sun and the fall of mean azimuthal motion with height above the mid-plane. We confirm that the Besançon-style Gaussian model accurately fits the GCS data, but fails to match the details of the more spatially extended RAVE survey. In particular, the Shu distribution function (DF) handles non-circular orbits more accurately and provides a better fit to the kinematic data. The Gaussian distribution function not only fits the data poorly but systematically underestimates the fall of velocity dispersion with radius. We find that correlations exist between a number of parameters, which highlights the importance of doing joint fits. The large size of the RAVE survey, allows us to get precise values for most parameters. However, large systematic uncertainties remain, especially in $V_{\odot}$ and $Θ_0$. We find that, for an extended sample of stars, $Θ_0$ is underestimated by as much as $10\%$ if the vertical dependence of the mean azimuthal motion is neglected. Using a simple model for vertical dependence of kinematics, we find that it is possible to match the Sgr A* proper motion without any need for $V_{\odot}$ being larger than that estimated locally by surveys like GCS.

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