Self-calibrating interloper bias in spectroscopic galaxy clustering
  surveys
  Contamination of interloper galaxies due to misidentified emission lines can
be a big issue in the spectroscopic galaxy clustering surveys, especially in
future high-precision observations. We propose a statistical method based on
the cross-correlations of the observational data itself between two redshift
bins to efficiently reduce this effect, and it also can derive the interloper
fraction f_i in a redshift bin with a high level of accuracy. The ratio of
cross and auto angular 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 the China Space Station Telescope (CSST). 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 also can be applied to other kinds of galaxy clustering
surveys like line intensity mapping.
