We present Intercut, a Python-based program that applies secondary line identification and photometric cuts to mock galaxy surveys, in order to simulate interloper identification. This program can be used to optimize the removal of interloper contamination in upcoming surveys. Intercut reads a mock galaxy survey and an emission line sensitivity and simulates interloper removal through secondary line identification and broad-band photometry. This program is designed to use the COSMOS mock catalog, although the program can be modified for an alternative mock catalog. The output of the program returns an interloper fraction for each emission line, as well as the total fraction over all lines, as a function of redshift. We test Intercut by predicting interloper rates for the WFIRST emission line sensitivity, finding agreement with previous work. This program is publically available on Github
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