用于恒星参数确定的深度学习应用：I-约束超参数
Deep Learning application for stellar parameters determination: I- Constraining the hyperparameters
摘要：
机器学习是一种有效的方法，用于分析和解释越来越多的可用天文数据。在这项研究中，我们展示了一种教学方法，这种方法应该有利于任何愿意在恒星参数确定的背景下尝试深度学习技术的人。利用卷积神经网络架构，我们给出了一个逐步概述如何选择最佳参数推导出最准确的恒星的恒星参数值：T $_{\rm{eff}}$，$\log g$，[X/H]，和$v_e \sin i$。随机噪声的合成光谱被用来约束这种方法，并模仿的意见。我们发现，每个恒星参数需要不同的网络超参数组合，达到的最大精度取决于这种组合，以及观测的信噪比和网络的架构。我们还表明，这种技术可以应用于其他光谱类型在不同的波长范围内的技术进行了优化后。
英文摘要：
Machine Learning is an efficient method for analyzing and interpreting the increasing amount of astronomical data that is available. In this study, we show, a pedagogical approach that should benefit anyone willing to experiment with Deep Learning techniques in the context of stellar parameters determination. Utilizing the Convolutional Neural Network architecture, we give a step by step overview of how to select the optimal parameters for deriving the most accurate values for the stellar parameters of stars: T$_{\rm{eff}}$, $\log g$, [X/H], and $v_e \sin i$. Synthetic spectra with random noise were used to constrain this method and to mimic the observations. We found that each stellar parameter requires a different combination of network hyperparameters and the maximum accuracy reached depends on this combination, as well as, the Signal to Noise ratio of the observations, and the architecture of the network. We also show that this technique can be applied to other spectral types in different wavelength ranges after the technique has been optimized.
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