Affiliation:
1. Department of Astronomy, School of Physics and Astronomy, Shanghai Jiao Tong University , 800 Dongchuan Road , Shanghai 200240, China
2. Key Laboratory for Particle Astrophysics and Cosmology (MOE)/Shanghai Key Laboratory for Particle Physics and Cosmology , Shanghai 200240 , China
Abstract
ABSTRACT
Interloper contamination due to line misidentification is an important issue in the future low-resolution spectroscopic surveys. We realize that the algorithm previously used for photometric redshift self-calibration, with minor modifications, can be particularly applicable to calibrate the interloper bias. In order to explore the robustness of the modified self-calibration algorithm, we construct the mock catalogues based on China Space Station Telescope (CSST), taking two main target emission lines, Hα and [O iii]. The self-calibration algorithm is tested in cases with different interloper fractions at 1 per cent, 5 per cent, and 10 per cent. We find that the interloper fraction and mean redshift in each redshift bin can be successfully reconstructed at the level of ∼ 0.002 and ∼ 0.001(1 + z), respectively. We also find the impact of the cosmic magnification can be significant, which is usually ignored in previous works, and therefore propose a convenient and efficient method to eliminate it. Using the elimination method, we show that the calibration accuracy can be effectively recovered with slightly larger uncertainty.
Funder
National Science Foundation of China
Shanghai Jiao Tong University
Publisher
Oxford University Press (OUP)
Subject
Space and Planetary Science,Astronomy and Astrophysics
Cited by
1 articles.
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1. Predicting interloper fraction with graph neural networks;Journal of Cosmology and Astroparticle Physics;2023-12-01