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Contamination of interloper galaxies due to misidentified emission lines canbe a big issue in the spectroscopic galaxy clustering surveys, especially infuture high-precision observations. We propose a statistical method based onthe cross-correlations of the observational data itself between two redshiftbins to efficiently reduce this effect, and it also can derive the interloperfraction f_i in a redshift bin with a high level of accuracy. The ratio ofcross and auto angular correlation functions or power spectra between redshiftbins are suggested to estimate f_i, and the key equations are derived fortheoretical discussion. In order to explore and prove the feasibility andeffectiveness of this method, we also run simulations, generate mock data, andperform cosmological constraints considering systematics based on theobservation of the China Space Station Telescope (CSST). We find that thismethod can effectively reduce the interloper effect, and accurately constrainthe cosmological parameters for f_i<1%~10%, which is suitable for most futuresurveys. This method also can be applied to other kinds of galaxy clusteringsurveys like line intensity mapping.

Self-calibrating interloper bias in spectroscopic galaxy clustering surveys