The China Space Station Telescope(CSST)is a telescope with 2 m diameter,obtaining images with high quality through wide-field observations.In its first observation cycle,to capture time-domain observation data,the CSS...The China Space Station Telescope(CSST)is a telescope with 2 m diameter,obtaining images with high quality through wide-field observations.In its first observation cycle,to capture time-domain observation data,the CSST is proposed to observe the Galactic halo across different epochs.These data have significant potential for the study of properties of stars and exoplanets.However,the density of stars in the Galactic center is high,and it is a well-known challenge to perform astrometry and photometry in such a dense star field.This paper presents a deep learning-based framework designed to process dense star field images obtained by the CSST,which includes photometry,astrometry,and classifications of targets according to their light curve periods.With simulated CSST observation data,we demonstrate that this deep learning framework achieves photometry accuracy of 2%and astrometry accuracy of 0.03 pixel for stars with moderate brightness mag=24(i band),surpassing results obtained by traditional methods.Additionally,the deep learning based light curve classification algorithm could pick up celestial targets whose magnitude variations are 1.7 times larger than magnitude variations brought by Poisson photon noise.We anticipate that our framework could be effectively used to process dense star field images obtained by the CSST.展开更多
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financial support provided by the National Natural Science Foundation of China(NSFC,grant Nos.12173027,11973028,11933001,1803012,12150009,and 12173062)the National Key R&D Program of China(2019YFA0706601)+3 种基金the Major Key Project of PCLthe science research grants received from the China Manned Space Project with Nos.CMS-CSST-2021-B12,CMS-CSST-2021-B09 and CMS-CSST-2021-A01the Square Kilometre Array(SKA)Project with No.2020SKA0110102the Civil Aerospace Technology Research Project(D050105)。
文摘The China Space Station Telescope(CSST)is a telescope with 2 m diameter,obtaining images with high quality through wide-field observations.In its first observation cycle,to capture time-domain observation data,the CSST is proposed to observe the Galactic halo across different epochs.These data have significant potential for the study of properties of stars and exoplanets.However,the density of stars in the Galactic center is high,and it is a well-known challenge to perform astrometry and photometry in such a dense star field.This paper presents a deep learning-based framework designed to process dense star field images obtained by the CSST,which includes photometry,astrometry,and classifications of targets according to their light curve periods.With simulated CSST observation data,we demonstrate that this deep learning framework achieves photometry accuracy of 2%and astrometry accuracy of 0.03 pixel for stars with moderate brightness mag=24(i band),surpassing results obtained by traditional methods.Additionally,the deep learning based light curve classification algorithm could pick up celestial targets whose magnitude variations are 1.7 times larger than magnitude variations brought by Poisson photon noise.We anticipate that our framework could be effectively used to process dense star field images obtained by the CSST.
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.