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arXiv 2301.05075astro-ph.EPastro-ph.SRcs.LG
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通过机器学习识别的HD 142666中嵌入原行星的运动学证据

Kinematic Evidence of an Embedded Protoplanet in HD 142666 Identified by Machine Learning

The University of Georgia(佐治亚大学);University of California, San Francisco(加州大学旧金山分校);Cardiovascular Research Institute(心血管研究所);The University of Alabama(阿拉巴马大学)

J. P. Terry, C. Hall, S. Abreau, S. Gleyzer

AI总结:

本研究利用机器学习模型在HD 142666原行星盘中发现局域化非开普勒运动,经流体动力学模拟验证,确认其存在一颗5木星质量的行星,为机器学习识别原行星盘非开普勒特征迈出第一步。

摘要:

原行星盘的观测表明,正在形成的系外行星会在盘的气体和尘埃上留下特征印记。在气体中,这些正在形成的系外行星会导致运动偏离开普勒运动,这可通过分子线观测探测到。我们之前的工作表明,机器学习能够正确判定这些盘中是否存在行星。利用我们的机器学习模型,我们在HD 142666的盘内识别出了强烈的局域化非开普勒运动。随后对一颗位于75 au处、质量为5木星质量的行星的系统进行的流体动力学模拟,重现了该运动学结构。按照该领域目前确立的标准,我们得出结论:HD 142666拥有一颗行星。这项工作是利用机器学习识别原行星盘中此前被忽略的非开普勒特征的第一步。

英文摘要:

Observations of protoplanetary disks have shown that forming exoplanets leave characteristic imprints on the gas and dust of the disk. In the gas, these forming exoplanets cause deviations from Keplerian motion, which can be detected through molecular line observations. Our previous work has shown that machine learning can correctly determine if a planet is present in these disks. Using our machine learning models, we identify strong, localized non-Keplerian motion within the disk HD 142666. Subsequent hydrodynamics simulations of a system with a 5 Jupiter-mass planet at 75 au recreates the kinematic structure. By currently established standards in the field, we conclude that HD 142666 hosts a planet. This work represents a first step towards using machine learning to identify previously overlooked non-Keplerian features in protoplanetary disks.

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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