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arXiv 2007.13779astro-ph.EP
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从原行星盘中观察到的间隙推断行星质量的机器学习模型

A Machine Learning model to infer planet masses from gaps observed in protoplanetary disks

Sayantan Auddy, Min-Kai Lin

摘要:

对明亮的原行星盘的观测经常显示出尘埃排放中的环形间隙。对这些间隙的一种解释是盘-行星相互作用。如果是这样的话,将行星间隙模型与观测到的原行星盘间隙拟合,就可以揭示隐藏行星的存在。然而,未来的调查预计将产生越来越多的原行星盘与差距。在这种情况下,由于磁盘行星相互作用的复杂性,为每个目标执行定制的拟合变得不切实际。为此,我们引入了DPNNet(磁盘行星神经网络),这是一种通过利用机器学习的力量来建立行星间隙的有效模型。我们训练一个深度神经网络,其中包含大量的尘埃盘行星流体动力学模拟,包括行星质量、盘温度、盘粘度、盘表面密度分布、粒子斯托克斯数和尘埃丰度。然后,可以部署网络来提取给定间隙形态的行星质量。在本系列的第一部分中,我们将重点介绍机器学习框架的基本概念。我们证明了它的实用性,将其应用到尘埃间隙中观察到的HL头周围的原行星盘在$10$ au,$30$ au,和$80$ au。我们的网络预测行星质量分别为$80 \, M_{\rm Earth}$、$63 \, M_{\rm Earth}$和$70 \, M_{\rm Earth}$,这与基于专门模拟的其他研究相当。我们讨论了我们的DPNNet的主要优势,它的灵活性,将新的物理,任何数量的参数和预测,其潜力,最终取代流体动力学模拟磁盘观察员和建模。

英文摘要:

Observations of bright protoplanetary disks often show annular gaps in their dust emission. One interpretation of these gaps is disk-planet interaction. If so, fitting models of planetary gaps to observed protoplanetary disk gaps can reveal the presence of hidden planets. However, future surveys are expected to produce an ever-increasing number of protoplanetary disks with gaps. In this case, performing a customized fitting for each target becomes impractical owing to the complexity of disk-planet interaction. To this end, we introduce DPNNet (Disk Planet Neural Network), an efficient model of planetary gaps by exploiting the power of machine learning. We train a deep neural network with a large number of dusty disk-planet hydrodynamic simulations across a range of planet masses, disk temperatures, disk viscosities, disk surface density profiles, particle Stokes numbers, and dust abundances. The network can then be deployed to extract the planet mass for a given gap morphology. In this work, first in a series, we focus on the basic concepts of our machine learning framework. We demonstrate its utility by applying it to the dust gaps observed in the protoplanetary disk around HL Tau at $10$ au, $30$ au, and $80$ au. Our network predict planet masses of $80 \, M_{\rm Earth}$, $63 \, M_{\rm Earth}$, and $70 \, M_{\rm Earth}$, respectively, which are comparable to other studies based on specialized simulations. We discuss the key advantages of our DPNNet in its flexibility to incorporate new physics, any number of parameters and predictions, and its potential to ultimately replace hydrodynamical simulations for disk observers and modelers.

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。

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