Self-calibrating Interloper Bias in Spectroscopic Galaxy-clustering Surveys

Abstract

Abstract Contamination of interloper galaxies due to misidentified emission lines can be a big issue in spectroscopic galaxy-clustering surveys, especially in future high-precision observations. We propose a statistical method based on the cross correlations of the observational data between two redshift bins to efficiently reduce this effect, and it can also derive the interloper fraction f i in a redshift bin with a high level of accuracy. The ratio of cross and autoangular correlation functions or power spectra between redshift bins are suggested to estimate f i, and the key equations are derived for theoretical discussion. In order to explore and prove the feasibility and effectiveness of this method, we also run simulations, generate mock data, and perform cosmological constraints considering systematics based on the observation of China Space Station Telescope. We find that this method can effectively reduce the interloper effect, and accurately constrain the cosmological parameters for f i < 1% ∼ 10%, which is suitable for most future surveys. This method can also be applied to other kinds of galaxy-clustering surveys like line intensity mapping.