Filtering interlopers from galaxy surveys

TL;DR

Intercut is a Python tool that simulates and optimizes the removal of interloper contamination in galaxy surveys using secondary line identification and photometric cuts, aiding future survey accuracy.

Contribution

The paper introduces Intercut, a novel Python program that models interloper removal in galaxy surveys, compatible with mock catalogs like COSMOS, and validated against existing data.

Findings

Intercut accurately predicts interloper rates for WFIRST sensitivity.

The program's predictions agree with previous studies.

Intercut provides interloper fractions as a function of redshift.

Abstract

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