论文标题
基于功率谱的卷积神经网络的亚巨星中的模式角度识别
Mode Angular Degree Identification in Subgiant Stars with Convolutional Neural Networks based on Power Spectrum
论文作者
论文摘要
识别振荡模式的角度$ l $ l $对于星星学是必不可少的，并且依赖于在所谓的峰出现分析中拟合功率谱之前的视觉标记。在振荡子级中，径向（$ L $ = 0）模式频率以频率线性分布，而非radial（$ l $> = 1）模式是P-G混合模式，其频率具有复杂的分布，这增加了识别$ L $的难度。在这项研究中，我们培训了一个1D卷积神经网络，使用平滑的振荡光谱执行此任务。通过训练模拟数据并微调预训练的网络，我们在开普勒数据上达到了95％的精度。
Identifying the angular degrees $l$ of oscillation modes is essential for asteroseismology and depends on visual tagging before fitting power spectra in a so-called peakbagging analysis. In oscillating subgiants, radial ($l$= 0) mode frequencies distributed linearly in frequency, while non-radial ($l$ >= 1) modes are p-g mixed modes that having a complex distribution in frequency, which increased the difficulty of identifying $l$. In this study, we trained a 1D convolutional neural network to perform this task using smoothed oscillation spectra. By training simulation data and fine-tuning the pre-trained network, we achieved a 95 per cent accuracy on Kepler data.
