arXivDaily arXiv每日学术速递 周一至周五更新
arXiv周末暂无论文更新,休息一下吧,周末愉快~~
arXiv 2006.05124astro-ph.GA
收藏

类星体宽Ly $α$发射线的深度学习预测

Deep Learning Prediction of Quasars Broad Ly$α$ Emission Line

Hassan Fathivavsari

摘要:

我们采用深度神经网络或深度学习来预测类星体光谱中宽Ly $α$发射线的通量和形状。我们使用17870高信噪比(SNR > 15)类星体光谱从斯隆数字巡天(SDSS)数据发布14(DR 14)训练模型,并评估其性能。SiIV、CIV和CIII]宽发射谱线用作神经网络的输入,并且模型返回预测的Ly $α$发射谱线作为输出。我们发现,我们的神经网络模型预测类星体连续体周围的Ly $α$光谱区域与$\sim$ 6 - 12%的精度和$\lesssim$ 1%的偏差。我们的模型可以用来估计食和鬼阻尼Ly $α$(DLA)吸收体的HI柱密度,因为这些系统中DLA吸收的存在强烈地污染了Ly $α$光谱区域周围类星体连续体的通量和形状。该模型也可用于研究星系际介质在再电离时期的状态。

英文摘要:

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.

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。

↑