Tracing Large-Scale Structure Morphology with Multiwavelength Line Intensity Maps

Abstract

Line intensity mapping (LIM) is an emerging technique for probing the large-scale structure (LSS) in the post-reionisation era. This captures the integrated flux of a particular spectral line emission from multiple sources within a patch of the sky without resolving them. Mapping different galaxy line emissions, such as the HI $21$-cm and CO rotational lines via LIM, can reveal complementary information about the bias with which the line emitters trace the underlying matter distribution and how different astrophysical phenomena affect the clustering pattern of these signals. The stage at which the structures in the "cosmic web" merge to form a single connected structure is known as the percolation transition. Using mock HI $21$-cm and CO($1-0$) LIM signals in the post-reionisation universe, we explore the connectivity of structures through percolation analysis and compare it with the underlying galaxy distribution. We probe the relative contributions of voids, filaments, and sheets to the galaxy density and line intensity maps using a morphological measure known as the local dimension. The CO($1-0$) map exhibits an increased filamentary behaviour and larger contribution from sheets than the $21$-cm map. We attempt to explain such an emission of the CO($1-0$) line from biased environments. The upcoming SKA-Mid will produce tomographic intensity maps of the $21$-cm signal at $z \lesssim 3$ in Band-1. CO maps can be produced at these redshifts in phase 2 of SKA-Mid, where the frequency coverage is expected to increase up to $\sim 50$ GHz. We present forecasts for the recovery of the local dimensions of these line intensity maps contaminated by thermal noise and line interlopers in SKA-Mid surveys.

Figures (12)

• Figure 1: Corresponding slices of the galaxy mass density $\rho_{\rm g}$ (left), $21$-cm brightness temperature $T_{21 \rm cm}$ (centre), and CO($1-0$) brightness temperature $T_{\rm CO}$ (right) maps at a spatial resolution $\delta x \simeq 0.87$ Mpc.
• Figure 2: Signal-to-noise ratios (SNR) of the $21$-cm and CO($1-0$) brightness temperature fluctuations at spatial resolution $\delta x \simeq 1$ Mpc in the overlapping redshift range where SKA1-Mid and SKA2-Mid can observe the $21$-cm and CO($1-0$) LIM signals, respectively. The noise estimates are made assuming Gaussian random thermal noise for $5000$ hours per pointing of SKA1-Mid observations and $100$ hours per pointing of SKA2-Mid observations for the $21$-cm and CO($1-0$) signals, respectively. The black dashed line corresponds to $z=1.41$, the redshift where both the $21$-cm and CO($1-0$) brightness temperature fluctuations have a significant SNR. The $1\sigma$ error bars denote standard deviation in SNR across $8 \, (150 \, {\rm Mpc})^3$ 21-cm subcubes and $27 \, (100 \, {\rm Mpc})^3$ CO($1-0$) subcubes, corresponding to realistic SKA-Mid surveys, due to cosmic variance in signal fluctuations.
• Figure 3: Illustration of the iterative coarse-graining scheme used for percolation analysis. The four panels show the same slice of a map without coarse-graining (first from left) and after 2, 4, and 6 iterations in order. On each iteration, the cells sharing a face with a bright cell are marked as bright. The maps are binary, with the dark cells marked by zeros and the bright cells by ones.
• Figure 4: Percolation curves for the galaxy mass density (solid black), $21$-cm (dashed red), and CO($1-0$) (dashed blue) maps at spatial resolution $\delta x = 0.5$ Mpc.
• Figure 5: Distribution of local dimensions of classifiable cells out of $10^5$ randomly sampled bright cells in the galaxy mass density (top), $21$-cm (middle), and CO($1-0$) (bottom) maps simulated with galaxy positions in real space. The $D$-values are binned into intervals of size 0.5. The different curves correspond to different length scales specified by the $R_{\rm min}$ and $R_{\rm max}$ values. The percentage of classifiable cells is mentioned alongside the length scale. The $1\sigma$ error bars denote standard deviation in $P(D)$ across $8 \, (150 \, {\rm Mpc})^3$ 21-cm subcubes and $27 \, (100 \, {\rm Mpc})^3$ CO($1-0$) subcubes, corresponding to realistic SKA-Mid survey areas of $4 \, {\rm deg}^2$ and $1.77 \, {\rm deg}^2$ respectively for $10^4$ randomly sampled bright cells.