Removal of interloper contamination to line-intensity maps using correlations with ancillary tracers of the large-scale structure

TL;DR

This paper introduces a model-independent method to remove interloper contamination in line-intensity mapping by leveraging correlations with external large-scale structure tracers, significantly improving signal clarity.

Contribution

The authors develop a novel, model-independent technique that uses statistical correlations with external tracers to effectively eliminate interloper contamination in LIM data.

Findings

Cleaning performance improves with higher correlation between interlopers and tracers.

Signal-to-noise ratio increases by up to a factor of 6 after cleaning.

Method validated against simulations, showing effective removal of large-scale contamination.

Abstract

Line-intensity mapping (LIM) offers an approach to obtain three-dimensional maps of the large-scale structure by collecting the aggregate emission from all emitters along the line of sight. The procedure hinges on reconstructing the radial positions of sources by relating the observed frequency to the rest-frame frequency of a target emission line. However, this step is hindered by `interloper-line' emission from different cosmological volumes that redshifts into the same observed frequency. In this work, we propose a model-independent technique to remove the contamination of line interlopers using their statistical correlation with external tracers of the large-scale structure, and identify the weights that minimize the variance of the cleaned field. Furthermore, we derive expressions for the resulting power spectra after applying our cleaning procedure, and validate them against simulations. We find that the cleaning performance improves as the correlation between the line interlopers and the external tracer increases, resulting in a gain in the signal-to-noise ratio of up to a factor 6 (2) for the auto- (cross-)power spectrum in idealized scenarios. This approach has the advantage of being model-independent, and is highly complementary to other techniques, as it removes large-scale clustering modes instead of individually masking the brightest sources of contamination.