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arXiv 2401.04571astro-ph.GA
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使用银河系盘动力学质量测量首次探测到旋臂

First spiral arm detection using dynamical mass measurements of the Milky Way disk

Axel Widmark, Aneesh P. Naik

AI总结:

本研究应用垂直Jeans方程分析银河系盘面密度的非轴对称变化,首次通过动力学质量测量探测到旋臂结构(相对过密度约20%,宽约400 pc),并推断薄盘面密度标长为3.3-4.2 kpc。

摘要:

我们将垂直Jeans方程应用于银河系盘,以研究薄盘面密度的非轴对称变化。我们将盘平面划分为网格间距为100 pc的面积单元,并使用Gaia DR3恒星的四个独立子集(通过绝对星等切割定义),探测距离达3 kpc。垂直Jeans方程由恒星数密度场和垂直速度场提供信息;对于前者,我们使用通过高斯过程回归生成的星图;对于后者,我们使用贝叶斯神经网络(Bayesian Neural Network)的径向速度预测,从而充分利用Gaia DR3自行运动样本的全部潜力。我们首次发现了以动力学测量的盘面密度过密形式存在的旋臂证据,该特征在所有四个数据样本中均被探测到,并且与通过恒星年龄和化学成分追踪的旋臂高度吻合。我们对该特征拟合了一个简单的旋臂模型,推断出相对过密度约为20%,宽度约为400 pc。当将分析限制在距离2 kpc以内的恒星时,我们还推断出薄盘面密度的标长为3.3--4.2 kpc。

英文摘要:

We apply the vertical Jeans equation to the Milky Way disk, in order to study non-axisymmetric variations in the thin disk surface density. We divide the disk plane into area cells with a 100 pc grid spacing, and use four separate subsets of the Gaia DR3 stars, defined by cuts in absolute magnitude, reaching distances up to 3 kpc. The vertical Jeans equation is informed by the stellar number density field and the vertical velocity field; for the former, we use maps produced via Gaussian Process regression; for the latter, we use Bayesian Neural Network radial velocity predictions, allowing us to utilize the full power of the Gaia DR3 proper motion sample. For the first time, we find evidence of a spiral arm in the form of an over-density in the dynamically measured disk surface density, detected in all four data samples, which also agrees very well with the spiral arm as traced by stellar age and chemistry. We fit a simple spiral arm model to this feature, and infer a relative over-density of roughly 20 % and a width of roughly 400 pc. We also infer a thin disk surface density scale length of 3.3--4.2 kpc, when restricting the analysis to stars within a distance of 2 kpc.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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