The abundant photometric data collected from multiple large-scale sky surveys give important opportunities for photometric redshift estimation.However,low accuracy is still a serious issue in the current photometric r...The abundant photometric data collected from multiple large-scale sky surveys give important opportunities for photometric redshift estimation.However,low accuracy is still a serious issue in the current photometric redshift estimation methods.In this paper,we propose a novel two-stage approach by integration of Self Organizing Map(SOM)and Convolutional Neural Network(CNN)methods together.The SOM-CNN method is tested on the dataset of 150000 galaxies from Sloan Digital Sky Survey Data Release 13(SDSS-DR13).Inthe first stage,we apply the SOM algorithm to photometric data clustering and divide the samples into early-type and late-type.In the second stage,the SOM-CNN model is established to estimate the photometric redshifts of galaxies.Next,the precision rate and recall rate curves(PRC)are given to evaluate the models of SOM-CNN and Back Propagation(BP).It can been seen from the PRC that the SOM-CNN model is better than BP,and the area of SOM-CNN is 0.94,while the BP is 0.91.Finally,we provide two key error indicators:mean square error(MSE)and Outliers.Our results show that the MSE of early-type is 0.0014 while late-type is 0.0019,which are better than the BP algorithm 22.2%and 26%,respectively.When compared with Outliers,our result is optimally 1.32%,while the K-nearest neighbor(KNN)algorithm has 3.93%.In addition,we also provide the error visualization figures aboutΔZ andδ.According to the statistical calculations,the early-type with an error of less than 0.1 accounts for 98.86%,while the late-type is 99.03%.This result is better than those reported in the literature.展开更多
基金supported by the Joint Research Fund in Astronomy(U1531242)under cooperative agreement between the National Natural Science Foundation of China(NSFC)and Chinese Academy of Sciences(CAS)。
文摘The abundant photometric data collected from multiple large-scale sky surveys give important opportunities for photometric redshift estimation.However,low accuracy is still a serious issue in the current photometric redshift estimation methods.In this paper,we propose a novel two-stage approach by integration of Self Organizing Map(SOM)and Convolutional Neural Network(CNN)methods together.The SOM-CNN method is tested on the dataset of 150000 galaxies from Sloan Digital Sky Survey Data Release 13(SDSS-DR13).Inthe first stage,we apply the SOM algorithm to photometric data clustering and divide the samples into early-type and late-type.In the second stage,the SOM-CNN model is established to estimate the photometric redshifts of galaxies.Next,the precision rate and recall rate curves(PRC)are given to evaluate the models of SOM-CNN and Back Propagation(BP).It can been seen from the PRC that the SOM-CNN model is better than BP,and the area of SOM-CNN is 0.94,while the BP is 0.91.Finally,we provide two key error indicators:mean square error(MSE)and Outliers.Our results show that the MSE of early-type is 0.0014 while late-type is 0.0019,which are better than the BP algorithm 22.2%and 26%,respectively.When compared with Outliers,our result is optimally 1.32%,while the K-nearest neighbor(KNN)algorithm has 3.93%.In addition,we also provide the error visualization figures aboutΔZ andδ.According to the statistical calculations,the early-type with an error of less than 0.1 accounts for 98.86%,while the late-type is 99.03%.This result is better than those reported in the literature.
