Clustering Confuses Spectro-photometry: An Investigation of 2D and 3D Forced Profile Matching for Stacking Line-intensity Mapping Data on Source Catalogues

TL;DR

This study enhances stacking techniques for line-intensity mapping by fitting observed signal profiles in 2D and 3D, improving detection significance by up to 25% in simulated COMAP-like data, and reveals the importance of profile shape and size.

Contribution

The paper introduces two new profile-fitting methods for stacking in LIM data, demonstrating their effectiveness and exploring the dependence of optimal profiles on signal properties.

Findings

Fitting methods improve detection significance by up to 25%.

Optimal profiles are larger than the instrument beam and Lorentzian in spectral shape.

Profile size and shape depend on redshift-space effects and tracer properties.

Abstract

Line-intensity mapping (LIM) is an emerging observational technique that is used to observe the universe on large scales at low resolution through spectral line emission. Stacking analyses coadd cutouts of LIM data on positions of known signal emitters, robustly detecting signal otherwise hidden in a noisy map. In this article, we present two augmentations of a stacking pipeline, both aiming to refine the sensitivity of the stack by assuming a specific observed signal shape in 2D spatial axes or 3D spatial and spectral axes, as well as stacking on source coordinates more precise than the resolution of the LIM data cube. We test these methods on a series of simplistic and complex simulations mimicking observations with the CO Mapping Array Project (COMAP) Pathfinder. We find that these fitting methods provide up to a 25% advantage in detection significance over the original stack method in realistic COMAP-like simulations. We also find that the optimal fitting profile, given our CO line model and galaxy model, is larger than the 5' width of the COMAP beam and takes on a Lorentzian shape in the spectral dimension. Our findings suggest a nuanced dependence of the optimal profile size and shape on the LIM signal itself, including redshift-space clustering and fingers-of-God effects that depend on the tracer luminosity function and bias.