Machine-learning Inference of the Interior Structure of Low-mass Exoplanets
Machine-learning Inference of the Interior Structure of Low-mass Exoplanets
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DOI：
10.3847/1538-4357/ab5d32
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发表时间：
2019-11
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通讯作者：
P. Baumeister;S. Padovan;N. Tosi;G. Montavon;N. Nettelmann;J. MacKenzie;M. Godolt
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作者：
P. Baumeister;S. Padovan;N. Tosi;G. Montavon;N. Nettelmann;J. MacKenzie;M. Godolt
我们探索了基于混合密度神经网络（mdn）的机器学习应用于受质量、半径和流体Love数k2约束的低质量系外行星的内部表征，其质量不超过25个地球质量。我们创建了一个90万颗人造行星的数据集，由富含铁的核心，硅酸盐地幔，高压冰壳和气体H/He包络组成，以行星质量和半径作为网络的输入来训练MDN。对于这种层状结构，我们表明MDN能够从行星的质量和半径推断出每个行星层可能厚度的分布。这种方法避免了为每个单独的行星使用一套专门的正演模型来计算这种分布的耗时任务。虽然富气体行星的特征可能是成分梯度，而不是明显的层，但这里提出的方法可以很容易地扩展到任何内部结构模型。流体勒夫数k2受到行星内部质量分布的限制，未来将对越来越多的系外行星进行测量。将k2作为MDN的输入显著降低了可能的内部结构的简并度。在开放的存储库中，我们提供经过训练的MDN，以便通过Python Notebook使用。
We explore the application of machine-learning based on mixture density neural networks (MDNs) to the interior characterization of low-mass exoplanets up to 25 Earth masses constrained by mass, radius, and fluid Love number, k2. We create a data set of 900,000 synthetic planets, consisting of an iron-rich core, a silicate mantle, a high-pressure ice shell, and a gaseous H/He envelope, to train a MDN using planetary mass and radius as inputs to the network. For this layered structure, we show that the MDN is able to infer the distribution of possible thicknesses of each planetary layer from mass and radius of the planet. This approach obviates the time-consuming task of calculating such distributions with a dedicated set of forward models for each individual planet. While gas-rich planets may be characterized by compositional gradients rather than distinct layers, the method presented here can be easily extended to any interior structure model. The fluid Love number k2 bears constraints on the mass distribution in the planets’ interiors and will be measured for an increasing number of exoplanets in the future. Adding k2 as an input to the MDN significantly decreases the degeneracy of the possible interior structures. In an open repository, we provide the trained MDN to be used through a Python Notebook.