Improving Photometric Redshift Estimation for CSST Mock Catalog Using SED Templates Calibrated with Perturbation Algorithm

Yicheng Li; Liping Fu; Zhu Chen; Zhijian Luo; Wei Du; Yan Gong; Xianmin Meng; Junhao Lu; Zhirui Tang; Pengfei Chen; Shaohua Zhang; Chenggang Shu; Xingchen Zhou; Zuhui Fan

arXiv · 2504.07684

Improving Photometric Redshift Estimation for CSST Mock Catalog Using SED Templates Calibrated with Perturbation Algorithm

Yicheng Li·Liping Fu·Zhu Chen·Zhijian Luo·Wei Du·Yan Gong·Xianmin Meng·Junhao Lu·Zhirui Tang·Pengfei Chen·Shaohua Zhang·Chenggang Shu·Xingchen Zhou·Zuhui Fan

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Abstract

Photometric redshifts of galaxies obtained by multi-wavelength data are widely used in photometric surveys because of its high efficiency. Although various methods have been developed, template fitting is still adopted as one of the most popular approaches. Its accuracy strongly depends on the quality of the Spectral Energy Distribution (SED) templates, which can be calibrated using broadband photometric data from galaxies with known spectroscopic redshifts. Such calibration is expected to improve photometric redshift accuracy, as the calibrated templates will align with observed photometric data more closely. The upcoming China Space Station Survey Telescope (CSST) is one of the Stage IV surveys, which aiming for high precision cosmological studies. To improve the accuracy of photometric redshift estimation for CSST, we calibrated the CWW+KIN templates using a perturbation algorithm with broadband photometric data from the CSST mock catalog. This calibration used a training set consisting of approximately 4,500 galaxies, which is 10% of the total galaxy sample. The outlier fraction and scatter of the photometric redshifts derived from the calibrated templates are 2.55% and 0.036, respectively. Compared to the CWW+KIN templates, these values are reduced by 34% and 23%, respectively. This demonstrates that SED templates calibrated with a small training set can effectively optimize photometric redshift accuracy for future large-scale surveys like CSST, especially with limited spectral training data.

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Yicheng Li, Liping Fu, Zhu Chen, Zhijian Luo, Wei Du, Yan Gong, Xianmin Meng, Junhao Lu, Zhirui Tang, Pengfei Chen, Shaohua Zhang, Chenggang Shu, Xingchen Zhou, Zuhui Fan. 2025-04-10. Improving Photometric Redshift Estimation for CSST Mock Catalog Using SED Templates Calibrated with Perturbation Algorithm. https://arxiv.org/abs/2504.07684

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