论文标题
来自Wise-PS1-strm的类星体的光度红移
Photometric redshifts for quasars from WISE-PS1-STRM
论文作者
论文摘要
三维宽视野星系调查是宇宙学研究的基础。对于较高的红移（z> 1.0），星系太微弱，类星体仍然追踪宇宙的大规模结构。由于可用的望远镜时间限制光谱调查，因此光度法可以有效地估算许多类星体的红移。最近，对于类星体光度红移而言，机器学习方法越来越成功，但是，它们取决于训练集的分布。因此，严格的可靠性估计至关重要。我们从明智的全天空和PS1 3 $π$ dr2天空调查的交叉匹配目录中提取了光学和红外光度数据。我们培训了XGBoost回归剂和一个人工神经网络，用于颜色指数与光谱红移之间的关系。我们使用K最近的邻居算法近似于有效的训练设置覆盖范围。我们估计可靠的光度红移为2,879,298种，与特征空间中的训练相关。我们通过独立的，基于聚类的红移估计技术验证了派生的红移。最终目录可公开使用。
Three-dimensional wide-field galaxy surveys are fundamental for cosmological studies. For higher redshifts (z > 1.0), where galaxies are too faint, quasars still trace the large-scale structure of the Universe. Since available telescope time limits spectroscopic surveys, photometric methods are efficient for estimating redshifts for many quasars. Recently, machine learning methods are increasingly successful for quasar photometric redshifts, however, they hinge on the distribution of the training set. Therefore a rigorous estimation of reliability is critical. We extracted optical and infrared photometric data from the cross-matched catalogue of the WISE All-Sky and PS1 3$π$ DR2 sky surveys. We trained an XGBoost regressor and an artificial neural network on the relation between color indices and spectroscopic redshift. We approximated the effective training set coverage with the K nearest neighbors algorithm. We estimated reliable photometric redshifts of 2,879,298 quasars which overlap with the training set in feature space. We validated the derived redshifts with an independent, clustering-based redshift estimation technique. The final catalog is publicly available.
