论文标题
使用机器学习在SDSS DR14中找到幽灵降低的$α$系统
Using Machine Learning to Find Ghostly Damped Ly$α$ Systems in SDSS DR14
论文作者
论文摘要
我们报告了Sloan Digital Sky Survey（SDSS）数据版本14（DR14）中发现59名新的幽灵吸收器的发现。这些吸收器，带有$ z {\ rm abs} $$ \ sim $$ z _ {\ rm qSO} $，没有显示ly $α$吸收，并且主要通过在光谱中检测到强金属吸收线的检测来识别。先前已知的系统的数量为30。在机器学习算法的帮助下，发现了新系统。 41（总计89个）吸收剂的光谱也涵盖了LY $β$频谱区域。通过将LY $β$吸收的阻尼机翼拟合在21（41个）吸收剂的堆叠频谱中，具有相对较强的LY $β$吸收，我们测量了log $ n $（hi）= 21.50的HI柱密度。该柱密度比以前的工作高0.5 dex。我们还发现，具有较强的$β$吸收的21种幽灵吸收剂的堆叠光谱中的金属吸收系具有与剩余系统堆叠频谱中的相似特性。这些间接证据强烈表明，我们的大多数幽灵吸收器确实是DLA。
We report the discovery of 59 new ghostly absorbers from the Sloan Digital Sky Survey (SDSS) Data Release 14 (DR14). These absorbers, with $z_{\rm abs}$$\sim$$z_{\rm QSO}$, reveal no Ly$α$ absorption, and they are mainly identified through the detection of strong metal absorption lines in the spectra. The number of previously known such systems is 30. The new systems are found with the aid of machine learning algorithms. The spectra of 41 (out of total of 89) absorbers also cover the Ly$β$ spectral region. By fitting the damping wings of the Ly$β$ absorption in the stacked spectrum of 21 (out of 41) absorbers with relatively stronger Ly$β$ absorption, we measured an HI column density of log$N$(HI)=21.50. This column density is 0.5dex higher than that of the previous work. We also found that the metal absorption lines in the stacked spectrum of the 21 ghostly absorbers with stronger Ly$β$ absorption have similar properties as those in the stacked spectrum of the remaining systems. These circumstantial evidence strongly suggest that the majority of our ghostly absorbers are indeed DLAs.
