Clustering of DESI galaxies split by thermal Sunyaev-Zeldovich effect

TL;DR

This work introduces a novel approach to cosmology by splitting DESI galaxies according to the tSZ signal in ACT DR6 maps, thereby probing different environmental densities and biases. The authors find that galaxies in higher tSZ SNR bins exhibit stronger large-scale clustering (higher bias) and larger line-of-sight velocity dispersions, even below the traditional SZ cluster-detection threshold. A simple simulation-based toy model is developed to reproduce the qualitative trends and test robustness against variations in the galaxy-halo connection, suggesting the tSZ-split clustering signal is fairly resilient to HOD choices. The study demonstrates that valuable cosmological information resides in the low-SNR tail of the tSZ map and outlines a path toward semi-analytical modeling and future joint analyses with next-generation CMB experiments (e.g., SO, CMB-S4) and DESI data to tighten cosmological constraints.

Abstract

The thermal Sunyaev-Zeldovich (tSZ) effect is associated with galaxy clusters - extremely large and dense structures tracing the dark matter with a higher bias than isolated galaxies. We propose to use the tSZ data to separate galaxies from redshift surveys into distinct subpopulations corresponding to different densities and biases independently of the redshift survey systematics. Leveraging the information from different environments, as in density-split and density-marked clustering, is known to tighten the constraints on cosmological parameters, like $Ω_m$, $σ_8$ and neutrino mass. We use data from the Dark Energy Spectroscopic Instrument (DESI) and the Atacama Cosmology Telescope (ACT) in their region of overlap to demonstrate informative tSZ splitting of Luminous Red Galaxies (LRGs). We discover a significant increase in the large-scale clustering of DESI LRGs corresponding to detections starting from 1-2 sigma in the ACT DR6 + Planck tSZ Compton-$y$ map, below the cluster candidate threshold (4 sigma). We also find that such galaxies have higher line-of-sight coordinate (and velocity) dispersions and a higher number of close neighbors than both the full sample and near-zero tSZ regions. We produce simple simulations of tSZ maps that are intrinsically consistent with galaxy catalogs and do not include systematic effects, and find a similar pattern of large-scale clustering enhancement with tSZ effect significance. Moreover, we observe that this relative bias pattern remains largely unchanged with variations in the galaxy-halo connection model in our simulations. This is promising for future cosmological inference from tSZ-split clustering with semi-analytical models. Thus, we demonstrate that valuable cosmological information is present in the lower signal-to-noise regions of the thermal Sunyaev-Zeldovich map, extending far beyond the individual cluster candidates.

Figures (12)

• Figure 1: Distribution of $\sigma_y$, the pixel standard deviation of Compton-$y$ parameter after our filtering (over the area of the overlap of ACT DR6 and DESI DR1 footprints). The black dashed vertical line shows the mean $\sigma_y$. The orange dashed horizontal line denotes CDF$=0.5$.
• Figure 2: Large-scale isotropic (monopole) cross-correlation functions of different SNR bins with the full LRG sample (excluding pairs with angular separation below 0.1 degrees). (In this and the following figures, we apply our fiducial Gaussian filter to the tSZ Compton-$y$ parameter map, and use the jackknife technique to estimate errorbars.) There is a significant clustering enhancement with increasing tSZ detection level even below the threshold for cluster candidates ACT-SZ-clusters-DR6. Colored dashed lines show the best fits obtained by scaling the full-sample autocorrelation function (black dashed line). The scaling coefficients are the relative biases shown in \ref{['fig:relbias-SNR-bins']}.
• Figure 3: Galaxy bias of different SNR bins relative to the full LRG sample. These numbers are based on the ratio of the corresponding correlation functions from \ref{['fig:clustering-SNR-bins']}. The bias increase (clustering enhancement) can be seen more concisely here. For smaller errorbars, which can not be seen well, we also show $3\sigma$ bars (exactly 3 times larger).
• Figure 4: Projected cross-correlation functions of different SNR bins with the full LRG sample. The line-of-sight separation limit is $\pi_{\max}=50~h^{-1}{\rm Mpc}$ (and we do not exclude pairs with small angular separations). Colored dashed lines show the best fits (using $r_p > 8~h^{-1}{\rm Mpc}$) obtained by scaling the full-sample autocorrelation function (black dashed line). The clustering enhancement is notable, as with the correlation function monopole (\ref{['fig:clustering-SNR-bins']}). The relative biases obtained from projected clustering are consistent with \ref{['fig:relbias-SNR-bins']}, but have larger errorbars.
• Figure 5: Small-scale line-of-sight cross-correlation functions of different SNR bins with the full LRG sample (not excluding pairs with small angular separations). The dashed lines show the best-fit exponentials according to \ref{['eq:xi-LoS-exponential']}. There is not only an increase in amplitude, but also a flattening of the slopes of the curves with increasing tSZ SNR, indicating regions with higher tSZ signal have hotter small-scale velocity dispersions.