论文标题
类星体的深度学习预测$α$排放线
Deep Learning Prediction of Quasars Broad Ly$α$ Emission Line
论文作者
论文摘要
我们采用了深层的神经网络，或者深度学习来预测类星体光谱中宽$α$发射线的通量和形状。我们使用Sloan Digital Sky Survey（SDSS）数据版本14（DR14）的17870高信噪比（SNR> 15）的类星体光谱来训练模型并评估其性能。 SIIV，CIV和CIII]宽发射线用作神经网络的输入，该模型将预测的$α$发射线返回作为输出。我们发现，我们的神经网络模型可以预测$ \ sim $ 6-12％精度和$ \ lyssim $ 1％的偏见，可预测$ \ sim $ \ sim $ \ sim的频谱区域。我们的模型可用于估计越来越多的$α$（DLA）吸收器的HI色谱柱密度，因为这些系统中DLA吸收的存在会极大地污染ly $ al $α$频谱区域周围的胶质连续体的通量和形状。该模型还可用于研究在电离时期期间的层间培养基状态。
We have employed 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 17870 high signal-to-noise ratio (SNR > 15) quasar spectra from the Sloan Digital Sky Survey (SDSS) Data Release 14 (DR14) to train the model and evaluate its performance. The SiIV, CIV, and CIII] 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 $\sim$6 - 12% precision and $\lesssim$1% bias. Our model can be used to estimate the HI column density of eclipsing and ghostly damped Ly$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 Ly$α$ spectral region. The model could also be used to study the state of the intergalactic medium during the epoch of reionization.
