岩石系外行星内部结构研究中的机器学习技术

IF 27.8 1区 物理与天体物理 Q1 ASTRONOMY & ASTROPHYSICS The Astronomy and Astrophysics Review Pub Date : 2021-06-01 DOI:10.1051/0004-6361/202140375
Yong Zhao, D. Ni
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引用次数: 1

摘要

上下文。由于观测技术的新发展,地球大小的系外行星已经被发现并具有特征,特别是那些可能具有与太阳系类地行星相当的岩石成分的行星。描述岩石系外行星的内部特征是研究其可居住性的主要目标之一。理论上的质量半径关系经常被用作约束岩石系外行星内部结构的工具。但是一条质量-半径曲线只能表示一个单一的内部结构,要得到符合给定质量和半径的所有可能的内部结构需要大量的计算时间。我们应用基于混合密度网络(mdn)的机器学习方法来研究岩石系外行星的内部。我们的目标是提供一个训练有素的MDN模型来快速有效地预测岩石系外行星的内部结构。我们提出了一组质量在0.1到10个地球质量之间的岩石系外行星的训练数据集,该数据集基于三层内部模型,假设成分与地球相似。然后使用该数据集训练MDN模型来预测岩石系外行星的层厚度和核心特性,其中行星质量、半径和含水量是MDN的输入。研究了训练后的MDN模型的性能,以了解其预测能力。通过与内部模型求解得到的实际解的比较,发现MDN模型在预测岩石系外行星的层厚和核心性质方面具有较好的性能。我们还将MDN模型应用于地球和超级地球系外行星LHS 1140b。在行星质量和半径的不确定性范围内,MDN的预测结果与内部模型的解符合得很好。更重要的是,与内部模型的计算成本相比,MDN模型的计算时间要短得多,为快速获取行星内部信息提供了方便而强大的工具。
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Machine learning techniques in studies of the interior structure of rocky exoplanets
Context. Earth-sized exoplanets have been discovered and characterized thanks to new developments in observational techniques, particularly those planets that may have a rocky composition that is comparable to terrestrial planets of the Solar System. Characterizing the interiors of rocky exoplanets is one of the main objectives in investigations of their habitability. Theoretical mass-radius relations are often used as a tool to constrain the internal structure of rocky exoplanets. But one mass-radius curve only represents a single interior structure and a great deal of computation time is required to obtain all possible interior structures that comply with the given mass and radius of a planet. Aims. We apply a machine-learning approach based on mixture density networks (MDNs) to investigate the interiors of rocky exoplanets. We aim to provide a well-trained MDN model to quickly and efficiently predict the interior structure of rocky exoplanets. Methods. We presented a training data set of rocky exoplanets with masses between 0.1 and 10 Earth masses based on three-layer interior models by assuming Earth-like compositions. This data set was then used to train the MDN model to predict the layer thicknesses and core properties of rocky exoplanets, where planetary mass, radius, and water content are inputs to the MDN. The performance of the trained MDN model was investigated in order to discern its predictive ability. Results. The MDN model is found to show good performance in predicting the layer thicknesses and core properties of rocky exoplanets through a comparison with the real solutions obtained by solving the interior models. We also applied the MDN model to the Earth and the super-Earth exoplanet LHS 1140b. The MDN predictions are in good agreement with the interior model solutions within the uncertainties of planetary mass and radius. More importantly, the MDN model takes a much shorter computational time compared to the cost of the interior model calculations, offering a convenient and powerful tool for quickly obtaining information on planetary interiors.
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来源期刊
The Astronomy and Astrophysics Review
The Astronomy and Astrophysics Review 地学天文-天文与天体物理
CiteScore
45.00
自引率
0.80%
发文量
7
期刊介绍: The Astronomy and Astrophysics Review is a journal that covers all areas of astronomy and astrophysics. It includes subjects related to other fields such as laboratory or particle physics, cosmic ray physics, studies in the solar system, astrobiology, instrumentation, and computational and statistical methods with specific astronomical applications. The frequency of review articles depends on the level of activity in different areas. The journal focuses on publishing review articles that are scientifically rigorous and easily comprehensible. These articles serve as a valuable resource for scientists, students, researchers, and lecturers who want to explore new or unfamiliar fields. The journal is abstracted and indexed in various databases including the Astrophysics Data System (ADS), BFI List, CNKI, CNPIEC, Current Contents/Physical, Chemical and Earth Sciences, Dimensions, EBSCO Academic Search, EI Compendex, Japanese Science and Technology, and more.
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