We have employed a deep neural network, or deep learning , to predict the flux and the shape of the broad Ly α emission lines in the spectra of quasars. We use 17,870 high signal-to-noise ratio (S/N > 15) quasar spectra from the Sloan Digital Sky Survey Data Release 14 to train the model and evaluate its performance. The Si iv , C iv , and C iii] broad emission lines are used as the input to the neural network, and the model returns the predicted Ly α emission line as the output. We found that our neural-network model predicts quasars’ continua around the Ly α spectral region with ∼6%–12% precision and ≲1% bias. Our model can be used to estimate the H i column density of eclipsing and ghostly damped Ly α (DLA) absorbers, as the presence of the DLA absorption in these systems strongly contaminates the flux and the shape of the quasar continuum around ...

中文翻译：

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类星体宽Lyα发射线的深度学习预测

我们采用了深度神经网络或深度学习来预测类星体光谱中的宽Ly发射线的通量和形状。我们使用Sloan Digital Sky Survey Data Release 14中的17,870个高信噪比（S / N> 15）类星体光谱来训练模型并评估其性能。Si iv，C iv和C iii]宽发射线用作神经网络的输入，模型返回预测的Lyα发射线作为输出。我们发现我们的神经网络模型以6％〜12％的精度和≲1％的偏差预测了Lyα光谱区域附近的类星体的连续性。我们的模型可用于估算日食和重影阻尼Lyα（DLA）吸收器的H i柱密度，