论文标题
使用人工神经网络对原球门磁盘进行建模：重新访问粘性磁盘模型和更新的磁盘质量
Modeling protoplanetary disk SEDs with artificial neural networks: Revisiting the viscous disk model and updated disk masses
论文作者
论文摘要
我们使用详细的磁盘模型和贝叶斯方法对金牛座恒星形成区域中23个原球门磁盘的光谱能量分布（SED）建模。通过将这些模型与人工神经网络相结合以极大地提高其性能，从而实现这一目标。这样的设置使我们能够与$α$ disk模型面对观察，同时考虑了几种不确定性和脱落。我们的结果产生了许多来源的高粘度和积聚率，这与磁盘中低湍流水平的最近测量不一致。这种不一致可能意味着粘度不是磁盘中角动量传输的主要机制，并且诸如磁盘风之类的替代方案在此过程中起着重要作用。我们还发现，SED衍生的磁盘质量比仅在（子）MM通量中获得的磁盘质量高，这表明部分磁盘发射仍然可以在（sub）mm波长处光学厚度厚。这种效果与磁盘种群研究特别相关，并减轻了原球磁盘和外球星系之间的先前观察性张力。
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.
