用人工神经网络模拟原行星盘SEDs：重访粘性盘模型和更新的盘质量
Modeling protoplanetary disk SEDs with artificial neural networks: Revisiting the viscous disk model and updated disk masses
摘要：
我们使用详细的盘模型和贝叶斯方法模拟了金牛座-御夫座恒星形成区的23个原行星盘的光谱能量分布（SED）。这是通过将这些模型与人工神经网络相结合来实现的，以大大加快其性能。这样的设置使我们能够面对$α$ -磁盘模型的观测，同时考虑到一些不确定性和退化。我们的结果产生高粘度和吸积率的许多来源，这是不一致的，最近的测量低湍流水平的磁盘。这种不一致性可能意味着粘性不是圆盘中角动量传输的主要机制，而其他的机制，如圆盘风，在这个过程中起着重要的作用。我们还发现，我们的SED派生磁盘质量系统高于那些只从（分）毫米通量，这表明部分磁盘发射仍然可以在（分）毫米波长的光学厚度。这一效应与星盘种群研究特别相关，并证实了以前观测到的原行星盘和系外行星系统质量之间的紧张关系。
英文摘要：
We model the spectral energy distributions (SEDs) of 23 protoplanetary disks in the Taurus-Auriga star-forming region using detailed disk models and a Bayesian approach. This is made possible by combining these models with artificial neural networks to drastically speed up their performance. Such a setup allows us to confront $α$-disk models with observations while accounting for several uncertainties and degeneracies. Our results yield high viscosities and accretion rates for many sources, which is not consistent with recent measurements of low turbulence levels in disks. This inconsistency could imply that viscosity is not the main mechanism for angular momentum transport in disks, and that alternatives such as disk winds play an important role in this process. We also find that our SED-derived disk masses are systematically higher than those obtained solely from (sub)mm fluxes, suggesting that part of the disk emission could still be optically thick at (sub)mm wavelengths. This effect is particularly relevant for disk population studies and alleviates previous observational tensions between the masses of protoplanetary disks and exoplanetary systems.
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