Characterizing Jupiter’s interior using machine learning reveals four key structures,Astronomy & Astrophysics

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Characterizing Jupiter’s interior using machine learning reveals four key structures
Astronomy & Astrophysics ( IF 5.4 ) Pub Date : 2024-12-18 , DOI: 10.1051/0004-6361/202452383
M. Ziv, E. Galanti, S. Howard, T. Guillot, Y. Kaspi

Context. The internal structure of Jupiter is constrained by the precise gravity field measurements by NASA’s Juno mission, atmospheric data from the Galileo entry probe, and Voyager radio occultations. Not only are these observations few compared to the possible interior setups and their multiple controlling parameters, but they remain challenging to reconcile. As a complex, multidimensional problem, characterizing typical structures can help simplify the modeling process.Aims. We explored the plausible range of Jupiter’s interior structures using a coupled interior and wind model, identifying key structures and effective parameters to simplify its multidimensional representation.Methods. We used NeuralCMS, a deep learning model based on the accurate concentric Maclaurin spheroid (CMS) method, coupled with a fully consistent wind model to efficiently explore a wide range of interior models without prior assumptions. We then identified those consistent with the measurements and clustered the plausible combinations of parameters controlling the interior.Results. We determine the plausible ranges of internal structures and the dynamical contributions to Jupiter’s gravity field. Four typical interior structures are identified, characterized by their envelope and core properties. This reduces the dimensionality of Jupiter’s interior to only two effective parameters. Within the reduced 2D phase space, we show that the most observationally constrained structures fall within one of the key structures, but they require a higher 1 bar temperature than the observed value.Conclusions. We provide a robust framework for characterizing giant planet interiors with consistent wind treatment, demonstrating that for Jupiter, wind constraints strongly impact the gravity harmonics while the interior parameter distribution remains largely unchanged. Importantly, we find that Jupiter’s interior can be described by two effective parameters that clearly distinguish the four characteristic structures and conclude that atmospheric measurements may not fully represent the entire envelope.

中文翻译：

使用机器学习表征木星内部揭示了四个关键结构

上下文。木星的内部结构受到美国宇航局朱诺号任务的精确重力场测量、伽利略进入探测器的大气数据和旅行者号射电掩星的限制。与可能的内部设置及其多个控制参数相比，这些观测结果不仅很少，而且它们仍然难以协调。作为一个复杂的多维问题，表征典型结构有助于简化建模过程。目标。我们使用内部和风耦合模型探索了 Jupiter 内部结构的合理范围，确定了关键结构和有效参数以简化其多维表示。方法。我们使用了 NeuralCMS，这是一种基于精确同心麦克劳林球体（CMS）方法的深度学习模型，并结合了完全一致的风模型，在没有先验假设的情况下有效地探索了广泛的内部模型。然后，我们确定了与测量结果一致的参数，并将控制内部的参数的合理组合进行了聚类。结果。我们确定了内部结构的合理范围和对木星引力场的动力学贡献。确定了四种典型的内部结构，以它们的围护结构和核心特性为特征。这将木星内部的维度降低到只有两个有效参数。在简化的 2D 相空间中，我们表明，观测约束最严格的结构属于关键结构之一，但它们需要比观测值高 1 bar 的温度。结论。我们提供了一个强大的框架来表征具有一致风处理的巨行星内部，证明对于木星，风约束强烈影响引力谐波，而内部参数分布基本保持不变。重要的是，我们发现木星的内部可以用两个有效参数来描述，这两个参数清楚地区分了四种特征结构，并得出结论，大气测量可能无法完全代表整个包络。