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 1805.04069astro-ph.GA
收藏

在缺乏H α和[NII]的情况下区别恒星形成星系和AGN星系:一种机器学习方法

The discrimination between star-forming and AGN galaxies in the absence of Hα and [NII]: A machine learning approach

Hossen Teimoorinia, Jared Keown

摘要:

在BPT图中没有H $α$和[NII](6584 nm)两条发射线的情况下,我们证明了其它光谱信息足以区分活动星系核和恒星形成星系。我们使用的模式识别方法和样本的星系光谱从斯隆数字巡天(SDSS)显示,在这次调查中,流量和等效宽度的[OIII](5007)和H $β$,随着4000-break,可以用来分类星系的BPT图。这种方法比使用恒星质量和[OIII]/H $β$的方法提供了更高的预测精度。首先,我们使用BPT图和各种物理参数来重新分类星系。接下来,使用混淆矩阵,我们确定“正确”预测的类以及混淆的情况。通过这种方式,我们研究了混淆矩阵中每个参数的影响,并对用于区分不同类别的物理参数进行了排名。例如,在这次调查中,我们表明,$\rm{g - r}$颜色可以提供与星系恒星质量相同的准确度来预测星系是否拥有活动星系核。最后,在相同的信息,我们还排名的参数参与的塞弗特和LINER星系的歧视。

英文摘要:

In the absence of the two emission lines H$α$ and [NII] (6584Å) in a BPT diagram, we show that other spectral information is sufficiently informative to distinguish AGN galaxies from star-forming galaxies. We use pattern recognition methods and a sample of galaxy spectra from the Sloan Digital Sky Survey (SDSS) to show that, in this survey, the flux and equivalent width of [OIII] (5007Å) and H$β$, along with the 4000Å-break, can be used to classify galaxies in a BPT diagram. This method provides a higher accuracy of predictions than those which use stellar mass and [OIII]/H$β$. First, we use BPT diagrams and various physical parameters to re-classify the galaxies. Next, using confusion matrices, we determine the `correctly' predicted classes as well as confused cases. In this way, we investigate the effect of each parameter in the confusion matrices and rank the physical parameters used in the discrimination of the different classes. We show that in this survey, for example, $\rm{g - r}$ colour can provide the same accuracy as galaxy stellar mass to predict whether or not a galaxy hosts an AGN. Finally, with the same information, we also rank the parameters involved in the discrimination of Seyfert and LINER galaxies.

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。

↑