Photo- $z$ with CuBAN $z$：一种使用聚类辅助反向传播神经网络的改进光度红移估计器
Photo-$z$ with CuBAN$z$: An improved photometric redshift estimator using Clustering aided Back Propagation Neural network
摘要：
我们提出了一个改进的光度红移估计代码，CuBAN $z$，这是公开的https：//goo.gl/fpk90V}{https：//goo.gl/fpk90V。它使用反向传播神经网络以及训练集的聚类，这使得它比现有的神经网络代码更有效。在CuBAN $z$中，训练集被分成几个自学习的星系团，这些星系在给定的跨度内具有相似的光度特性和光谱红移。聚类算法使用颜色信息（即，$u-g$、$g-r$等）。而不是在各个光度带的视星等，因为光度红移对不同波段之间的通量差而不是实际值更敏感。使用测量中所有可能的颜色、幅度和不确定性为每个聚类训练单独的神经网络。对于一个红移未知的星系，我们识别出具有相似光度特性的最接近的可能星系团，并使用这些星系团来获得光度红移，使用这些星系团成员训练的特定网络。对于与任何训练集群都不匹配的星系，光度红移是从使用整个训练集的单独网络中获得的。这种聚类方法使我们能够更准确地确定红移。这里使用SDSS Stripe 82目录来演示代码。对于具有红移范围$z_{\rm spec}<0.7$的聚类源，在训练/测试阶段中的残余误差（$\langle (z_{\rm spec}-z_{\rm phot})^2\rangle^{1/2} $）低至0.03，与现有的ANNz码相比，现有的ANNz码在相同的测试数据集上提供0.05的残余误差。此外，我们提供了一个更好的估计所得出的光度红移的不确定性。
英文摘要：
We present an improved photometric redshift estimator code, CuBAN$z$, that is publicly available at https://goo.gl/fpk90V}{https://goo.gl/fpk90V. It uses the back propagation neural network along with clustering of the training set, which makes it more efficient than existing neural network codes. In CuBAN$z$, the training set is divided into several self learning clusters with galaxies having similar photometric properties and spectroscopic redshifts within a given span. The clustering algorithm uses the color information (i.e. $u-g$, $g-r$ etc.) rather than the apparent magnitudes at various photometric bands as the photometric redshift is more sensitive to the flux differences between different bands rather than the actual values. Separate neural networks are trained for each cluster using all possible colors, magnitudes and uncertainties in the measurements. For a galaxy with unknown redshift, we identify the closest possible clusters having similar photometric properties and use those clusters to get the photometric redshifts using the particular networks that were trained using those cluster members. For galaxies that do not match with any training cluster, the photometric redshifts are obtained from a separate network that uses entire training set. This clustering method enables us to determine the redshifts more accurately. SDSS Stripe 82 catalog has been used here for the demonstration of the code. For the clustered sources with redshift range $z_{\rm spec}<0.7$, the residual error ($\langle (z_{\rm spec}-z_{\rm phot})^2\rangle^{1/2} $) in the training/testing phase is as low as 0.03 compared to the existing ANNz code that provides residual error on the same test data set of 0.05. Further, we provide a much better estimate of the uncertainty of the derived photometric redshift.
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。
