Spectral classification indicators of emission-line galaxies from the Sloan Digital Sky Survey
Spectral classification indicators of emission-line galaxies from the Sloan Digital Sky Survey
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DOI：
10.1093/mnras/stv975
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发表时间：
2015-07
影响因子：
4.8
通讯作者：
F. Shi;Yu-Yan Liu;Pei-Yu Li;Ming Yu;Yu-Ming Lei;Jian Wang
中科院分区：
文献类型：
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作者：
F. Shi;Yu-Yan Liu;Pei-Yu Li;Ming Yu;Yu-Ming Lei;Jian Wang
为了找到有效的光谱分类图来对发射线星系进行分类，特别是在大型调查和庞大的数据库中，本文将人工神经网络（ANN）监督学习算法应用于马克斯普朗克研究所和约翰霍普金斯大学（MPA/JHU） （http://www.sdss3.org/dr9/spectro/spectroaccess.php）提供的斯隆数字巡天数据发布9中的发射线星系样本。采用两步法。(i)人工神经网络必须使用已知为活动星系核（agn）宿主、复合体或恒星形成星系的物体子集进行训练，将强发射在线通量测量值作为输入特征向量，其中为强发射在线通量比的数量。（ii）网络对一个星系样本进行训练后，剩余的星系在自动测试分析中被分类为AGN宿主星系、复合星系或造星星系。我们表明，基于[Nii]/Hα与其他发射在线比的分类图，如[Oiii]/Hβ， [Neiii]/[Oii], ([Oiii]λ4959 + [Oiii]λ5007)/[Oiii]λ4363, [Oii]/Hβ, [Ariii]/[Oiii]， [Sii]/Hα和[Oi]/Hα，加上颜色，使我们能够明确地分离AGN宿主，复合体或恒星形成星系。其中，[Nii]/Hα与[Oiii]/Hβ的图对AGN宿主、复合体或恒星形成星系的分类准确率达到98%。除了[Nii]/Hα与[Oiii]/Hβ的图外，上面的其他图的精度为~ 90%。论文中的代码可在网络上获得（http://fshi5388.blog.163.com）。
To find efficient spectral classification diagrams to classify emission-line galaxies, especially in large surveys and huge data bases, an artificial neural network (ANN) supervised learning algorithms is applied to a sample of emission-line galaxies from the Sloan Digital Sky Survey data release 9 provided by the Max Planck Institute and the Johns Hopkins University (MPA/JHU) (http://www.sdss3.org/dr9/spectro/spectroaccess.php). A two-step approach is adopted. (i) The ANN network must be trained with a subset of objects that are known to be active galactic nuclei (AGNs) hosts, composites or star-forming galaxies, treating the strong emission-line flux measurements as input feature vectors inn-dimensional space, wherenis the number of strong emission-line flux ratios. (ii) After the network is trained on a sample of galaxies, the remaining galaxies are classified in the automatic test analysis as AGN hosts, composites or star-forming galaxies. We show that the classification diagrams based on the [Nii]/Hα versus other emission-line ratio, such as [Oiii]/Hβ, [Neiii]/[Oii], ([Oiii]λ4959 + [Oiii]λ5007)/[Oiii]λ4363, [Oii]/Hβ, [Ariii]/[Oiii], [Sii]/Hα, and [Oi]/Hα, plus colour, allows us to separate unambiguously AGN hosts, composites or star-forming galaxies. Among them, the diagram of [Nii]/Hα versus [Oiii]/Hβ achieved an accuracy of 98 per cent for classification of AGN hosts, composites or star-forming galaxies. The other diagrams above except the diagram of [Nii]/Hα versus [Oiii]/Hβ give an accuracy of ∼90 per cent. The code in the paper is available on the web (http://fshi5388.blog.163.com).