4.7 Article

Classify and Explore the Diversity of Planetary Population and Interior Properties

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ASTROPHYSICAL JOURNAL
卷 957, 期 1, 页码 -

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IOP Publishing Ltd
DOI: 10.3847/1538-4357/acf0bf

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This study explores the diversity and evolution relations of planets using synthetic populations. Six outstanding clusters are detected in mass-radius space, and typical planets are extracted for each type. The classification includes gas-poor planets, gas-rich planets, and the significant role of giant gas planets in shaping the system through orbital resonance.
Classification is an essential method and has been developed widely in astronomy. However, planets still lack a universal classification framework, because the solar system planet sample is too small for statistical analysis. Fortunately, exoplanets supply large samples to help build up synthetic planetary populations then support a classification framework. In this study, we use synthetic populations to explore the diversity and evolution relations of planets. We detect six outstanding clusters in mass-radius space with the kernel density estimation and extract typical planets for each type. The first four types are gas-poor planets, and the last two are gas-rich. For an intermediate type, the light gas envelopes contribute to the observable radius but not the mass. Once the planet is massive enough (3.9 M J ), its size shrinks with increasing mass due to self-gravity. Based on the evolution tracks and the gas envelopes' properties, the environment is linked strongly to the gas properties, and it controls which type can form at a specific location. The system with gas giants will be different from those without, including total planet mass and the number of planets in the system. Giant planets shape the whole system by orbital resonance. Each type of planets' period ratios are different, and gas giants have the most outstanding accumulation peak at 2:1 resonance. In the future, the patterns of observed planets' retrieved interior structures can help to confirm the suggested classification. However, the structure degeneracy induces high uncertainty, such that the framework will still profit from additional theoretical constraints.

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