Three-Dimensional Stacking as a Line Intensity Mapping Statistic

TL;DR

This work tackles how three-dimensional stacking of line intensity mapping (LIM) data on the positions of galaxies can validate auto-correlation detections and constrain galaxy properties. A flexible joint simulation pipeline is developed to generate concurrent LIM (CO(1--0)) maps and Lyα galaxy catalogs for the same dark matter halos, incorporating correlated scatter, interlopers, and redshift uncertainties. Systematic exploration across stack parameters, catalogue design, LIM resolution, and astrophysical models reveals that the stack signal is dominated by neighbors in large-scale clustering rather than the catalogued halos themselves, and that an optimal setup combines a large spectroscopic catalogue targeting high-mass halos with a high-spectral-resolution LIM tracer. The results indicate stacking is a valuable, robust cross-check for LIM detections and will be most powerful when complemented by full cross-correlation analyses to break degeneracies between tracer physics and cosmology.

Abstract

Line-intensity mapping (LIM) is a growing technique that measures the integrated spectral-line emission from unresolved galaxies over a three-dimensional region of the Universe. Although LIM experiments ultimately aim to provide powerful cosmological constraints via auto-correlation, many LIM experiments are also designed to take advantage of overlapping galaxy surveys, enabling joint analyses of the two datasets. We introduce a flexible simulation pipeline that can generate mock galaxy surveys and mock LIM data simultaneously for the same population of simulated galaxies. Using this pipeline, we explore a simple joint analysis technique: three-dimensional co-addition (stacking) of LIM data on the positions of galaxies from a traditional galaxy catalogue. We test how the output of this technique reacts to changes in experimental design of both the LIM experiment and the galaxy survey, its sensitivity to various astrophysical parameters, and its susceptibility to common systematic errors. We find that an ideal catalogue for a stacking analysis targets as many high-mass dark matter halos as possible. We also find that the signal in a LIM stacking analysis originates almost entirely from the large-scale clustering of halos around the catalogue objects, rather than the catalogue objects themselves. While stacking is a sensitive and conceptually simple way to achieve a LIM detection, thus providing a valuable way to validate a LIM auto-correlation detection, it will likely require a full cross-correlation to achieve further characterization of the galaxy tracers involved, as the cosmological and astrophysical parameters we explore here have degenerate effects on the stack.

Figures (24)

• Figure 1: Flowchart depicting the multi-tracer simulation pipeline. Orange boxes indicate steps which affect both the galaxy catalogue and LIM data, blue boxes are actions on the simulated LIM data, and purple boxes are actions on the simulated galaxy catalogue. Steps with white boxes are optional.
• Figure 2: Zoomed-in frequency slices of the simulated population of DM halos and resulting mock observations. Left: the DM halos, coloured by their halo mass. Centre: the mock LIM fluctuation map of the CO emission (with no noise added). Right: the Ly$\alpha$ luminosity of each DM halo. The halos which would actually be detected by the mock survey, and thus are included in the galaxy catalogue, are shown as larger cyan circles in all three panels.
• Figure 3: The CO Luminosity as a function of halo DM mass for the different CO models tested. The models are listed in §\ref{['sec:simpipeline:co_luminosity_sim']}.
• Figure 4: Top: The various luminosity functions used to model the $L_\mathrm{Ly\alpha}$ assigned to each DM halo to generate the galaxy catalogue being stacked. Each model is a Schechter function, with parameters described in §\ref{['sec:simpipeline:lya_luminosity_sim']}. Bottom: the resulting luminosity as a function of DM halo mass.
• Figure 5: One- and two-dimensional projections of a simulated stack cubelet generated using the default simulation parameters. The top row shows a simulation realization with no added noise (variations are due to individual simulated halos), and the bottom row shows the same simulation realization with added radiometer noise (following §\ref{['sec:simpipeline:simmap']}). Left: the frequency spectrum of the central stack aperture. The $N_\mathrm{chan}$ frequency channels which are integrated over to generate the final stack luminosity are highlighted in grey. Center: a spatial profile of the stack, determined by summing over the three central frequency channels (those highlighted in the spectrum) into an image (shown in the right panel) and then collapsing the RA axis by summing over the three central spaxels. The spatial profile plots this quantity as a function of the angular offset in the declination direction from the stack centre. As in the spectrum, the width of the spatial aperture over which the emission is integrated to generate the final stack luminosity is highlighted in grey. Right: the 2D image, with the $N_\mathrm{spax}\times N_\mathrm{spax}$ spatial aperture boxed in black.