利用条件可逆神经网络推断系外行星形成
Exoplanet formation inference using conditional invertible neural networks
AI总结：
本文利用条件可逆神经网络推断系外行星形成过程，通过多行星系统数据提升预测能力，但单行星数据存在外推局限。
摘要：
对观测到的系外行星起源的解释通常仅能定性地进行，因为行星形成模型中的关键参数存在不确定性。为了允许一种能够追溯时间回行星诞生位置的定量方法，我们训练了最近开发的条件可逆神经网络（cINN）在由全球行星形成模型生成的合成数据上，该模型追踪从尘埃颗粒到最终形成的大行星的生长过程。除了确定性的单行星形成运行外，我们还包含了在多行星系统中相互作用的行星，这些系统包含了一些混沌的度量。对于后者情况，我们将它们视为独立行星，或选择最有可能被望远镜发现的两到三个行星。我们发现，对多行星数据进行训练，每个行星视为独立点，具有前景。单行星数据仅覆盖了少量行星，无法很好地外推到训练数据中未包含的行星特性。扩展到行星系统需要更多的训练数据，因为问题的维度更高。
英文摘要：
The interpretation of the origin of observed exoplanets is usually done only qualitatively due to uncertainties of key parameters in planet formation models. To allow a quantitative methodology which traces back in time to the planet birth locations, we train recently developed conditional invertible neural networks (cINN) on synthetic data from a global planet formation model which tracks growth from dust grains to evolved final giant planets. In addition to deterministic single planet formation runs, we also include gravitationally interacting planets in multiplanetary systems, which include some measure of chaos. For the latter case, we treat them as individual planets or choose the two or three planets most likely to be discovered by telescopes. We find that training on multiplanetary data, each planet treated as individual point, is promising. The single-planet data only covers a small range of planets and does not extrapolate well to planet properties not included in the training data. Extension to planetary systems will require more training data due to the higher dimensionality of the problem.
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。
