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arXiv 2103.14039astro-ph.GAhep-phphysics.data-an
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机器学习第六维度:来自5D相空间相关性的恒星径向速度

Machine Learning the 6th Dimension: Stellar Radial Velocities from 5D Phase-Space Correlations

Adriana Dropulic, Bryan Ostdiek, Laura J. Chang, Hongwan Liu, Timothy Cohen, Mariangela Lisanti

摘要:

盖亚卫星将观测超过10亿颗银河系恒星的位置和速度。在早期的数据发布中,大多数观测到的恒星都没有完整的6D相空间信息。在这封信中,我们证明了推断丢失的视线速度的能力,直到更多的光谱观测变得可用。我们利用一种新型的神经网络架构,在对具有完整相空间信息的数据子集进行训练后,接收恒星的5D天体测量(角坐标,自行和视差)并输出预测的视线速度与相关的不确定性。与模拟盖亚目录工作,我们表明,该网络可以成功地恢复每个速度分量的分布和相关性的恒星落在太阳的~5千秒差距。我们还表明,该网络可以准确地重建速度分布的恒星晕中的运动子结构是空间均匀的,即使它包括一小部分的总恒星数。

英文摘要:

The Gaia satellite will observe the positions and velocities of over a billion Milky Way stars. In the early data releases, the majority of observed stars do not have complete 6D phase-space information. In this Letter, we demonstrate the ability to infer the missing line-of-sight velocities until more spectroscopic observations become available. We utilize a novel neural network architecture that, after being trained on a subset of data with complete phase-space information, takes in a star's 5D astrometry (angular coordinates, proper motions, and parallax) and outputs a predicted line-of-sight velocity with an associated uncertainty. Working with a mock Gaia catalog, we show that the network can successfully recover the distributions and correlations of each velocity component for stars that fall within ~5 kpc of the Sun. We also demonstrate that the network can accurately reconstruct the velocity distribution of a kinematic substructure in the stellar halo that is spatially uniform, even when it comprises a small fraction of the total star count.

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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