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Tracing the Galactic Halo: Obtaining Bayesian mass estimates of the Galaxy in the presence of incomplete data

机译:追踪银河系:在不完全数据存在下获得银河系的贝叶斯质量估计

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The mass and cumulative mass profile of the Galaxy are its most fundamental properties. Estimating these properties, however, is not a trivial problem. We rely on the kinematic information from Galactic satellites such as globular clusters and dwarf galaxies, and this data is incomplete and subject to measurement uncertainty. In particular, the complete 3D velocity vectors of objects are sometimes unavailable, and there may be selection biases due to both the distribution of objects around the Galaxy and our measurement position. On the other hand, the uncertainties of these data are fairly well understood. Thus, we would like to incorporate these uncertainties and the incomplete data into our estimate of the Milky Way's mass. The Bayesian paradigm offers a way to deal with both the missing kinematic data and measurement errors using a hierarchical model. An application of this method to the Milky Way halo mass profile, using the kinematic data for globular clusters and dwarf satellites, is shown.
机译:星系的质量和累积质量剖面是其最基本的性质。然而,估计这些属性不是一个微不足道的问题。我们依靠来自Galactic卫星的运动信息,例如球状群和矮星,该数据不完整,并受测量不确定性。特别地,物体的完整3D速度矢量有时是不可用的,并且由于星系周围的物体的分布和我们的测量位置,可能存在选择偏差。另一方面,这些数据的不确定性得到了很好的理解。因此,我们希望将这些不确定性和不完整的数据纳入我们对银河系的估计。贝叶斯范式提供了一种方法,可以使用分层模型处理缺少的运动数据和测量误差。将该方法应用于银河系Halo质量型材,使用球簇和矮化卫星的运动学数据。

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