  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
