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arXiv 2103.13394astro-ph.SR
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使用深度学习的$γ$ Doradus恒星的星震学约束恒星演化理论

Constraining stellar evolution theory with asteroseismology of $γ$ Doradus stars using deep learning

Joey S. G. Mombarg, Timothy Van Reeth, Conny Aerts

摘要:

中等质量恒星内部的角动量和化学元素的传输效率缺乏适当的校准,从而在恒星的演化路径上引入了不确定性。改进需要更好地估计恒星质量,演化阶段和内部混合特性。我们的目标是开发一个神经网络方法的asteroseismic建模和测试其能力,提供恒星的质量,年龄和超调参数的样本37 $γ$剑鱼座恒星。在这里,我们的目标是执行参数估计从连续径向顺序的偶极模式测量的各个周期的建模。我们已经训练了神经网络来预测高阶重力模式的理论脉动周期以及给定质量,年龄,过冲参数,扩散包络混合,金属丰度和近核旋转频率的光度,有效温度和表面重力。我们已经应用我们的神经网络计算脉动周期和光球观测,C-3 PO,我们的样本和计算网格的恒星脉动模型的估计参数。我们提出的近核心的旋转速率(从文献)作为推断的恒星年龄和临界旋转速率的函数。我们评估的旋转速率附近的主要序列的开始假设刚性旋转的样品。此外,我们测量的核心过冲区域的程度,并没有发现与质量,年龄,或旋转的相关性。在这项研究中开发的神经网络方法允许推导恒星属性占主导地位的恒星演化-如质量,年龄和核心边界混合的程度。它还开辟了一条道路,为未来估计整个辐射包络的混合配置文件,其目的是推断这些配置文件的大样本的$γ$剑鱼座恒星。

英文摘要:

The efficiency of the transport of angular momentum and chemical elements inside intermediate-mass stars lacks proper calibration, thereby introducing uncertainties on a star's evolutionary pathway. Improvements require better estimation of stellar masses, evolutionary stages, and internal mixing properties. We aim to develop a neural network approach for asteroseismic modelling and test its capacity to provide stellar masses, ages, and overshooting parameter for a sample of 37 $γ$ Doradus stars. Here, our goal is to perform the parameter estimation from modelling of individual periods measured for dipole modes with consecutive radial order. We have trained neural networks to predict theoretical pulsation periods of high-order gravity modes as well as the luminosity, effective temperature, and surface gravity for a given mass, age, overshooting parameter, diffusive envelope mixing, metallicity, and near-core rotation frequency. We have applied our neural networks for Computing Pulsation Periods and Photospheric Observables, C-3PO, to our sample and compute grids of stellar pulsation models for the estimated parameters. We present the near-core rotation rates (from literature) as a function of the inferred stellar age and critical rotation rate. We assess the rotation rates of the sample near the start of the main sequence assuming rigid rotation. Furthermore, we measure the extent of the core overshoot region and find no correlation with mass, age, or rotation. The neural network approach developed in this study allows for the derivation of stellar properties dominant for stellar evolution -- such as mass, age, and extent of core-boundary mixing. It also opens a path for future estimation of mixing profiles throughout the radiative envelope, with the aim to infer those profiles for large samples of $γ$ Doradus stars.

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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