Extracting photometric redshift from galaxy flux and image data using neural networks in the CSST survey

Author:

Zhou Xingchen12,Gong Yan13ORCID,Meng Xian-Min1,Cao Ye12,Chen Xuelei245ORCID,Chen Zhu6,Du Wei6,Fu Liping6ORCID,Luo Zhijian6

Affiliation:

1. Key Laboratory of Space Astronomy and Technology, National Astronomical Observatories, Chinese Academy of Sciences, 20A Datun Road, Beijing 100101, China

2. University of Chinese Academy of Sciences, NAOC, Beijing 100049, China

3. Science Center for China Space Station Telescope, National Astronomical Observatories, Chinese Academy of Sciences, 20A Datun Road, Beijing 100101, China

4. Key Laboratory for Computational Astrophysics, National Astronomical Observatories, Chinese Academy of Sciences, 20A Datun Road, Beijing 100101, China

5. Centre for High Energy Physics, Peking University, Beijing 100871, China

6. Shanghai Key Lab for Astrophysics, Shanghai Normal University, Shanghai 200234, China

Abstract

ABSTRACT The accuracy of galaxy photometric redshift (photo-z) can significantly affect the analysis of weak gravitational lensing measurements, especially for future high-precision surveys. In this work, we try to extract photo-z information from both galaxy flux and image data expected to be obtained by China Space Station Telescope (CSST) using neural networks. We generate mock galaxy images based on the observational images from the Advanced Camera for Surveys of Hubble Space Telescope (HST-ACS) and COSMOS catalogues, considering the CSST instrumental effects. Galaxy flux data are then measured directly from these images by aperture photometry. The multilayer perceptron (MLP) and convolutional neural network (CNN) are constructed to predict photo-z from fluxes and images, respectively. We also propose to use an efficient hybrid network, which combines the MLP and CNN, by employing the transfer learning techniques to investigate the improvement of the result with both flux and image data included. We find that the photo-z accuracy and outlier fraction can achieve σNMAD = 0.023 and $\eta = 1.43{{\ \rm per\ cent}}$ for the MLP using flux data only, and σNMAD = 0.025 and $\eta = 1.21{{\ \rm per\ cent}}$ for the CNN using image data only. The result can be further improved in high efficiency as σNMAD = 0.020 and $\eta = 0.90{{\ \rm per\ cent}}$ for the hybrid transfer network. These approaches result in similar galaxy median and mean redshifts 0.8 and 0.9, respectively, for the redshift range from 0 to 4. This indicates that our networks can effectively and properly extract photo-z information from the CSST galaxy flux and image data.

Funder

MOST

NSFC

CAS

National Natural Science Foundation of China

Chinese Academy of Sciences

China Manned Space

Publisher

Oxford University Press (OUP)

Subject

Space and Planetary Science,Astronomy and Astrophysics

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