Cross Correlation between the Thermal Sunyaev–Zel’dovich Effect and Projected Galaxy Density Field

Abstract

We present a joint analysis of the power spectra of the Planck Compton y parameter map and the projected galaxy density field using the Wide Field Infrared Survey Explorer (WISE) all-sky survey. We detect the statistical correlation between WISE and Planck data (gy) with a significance of 21.8σ. We also measure the autocorrelation spectrum for the thermal Sunyaev–Zel’dovich (tSZ) (yy) and the galaxy density field maps (gg) with a significance of 150σ and 88σ, respectively. We then construct a halo model and use the measured correlations C ℓ gg , C ℓ yy , and C ℓ g y to constrain the tSZ mass bias B ≡ M 500 / M 500 tSZ . We also fit for the galaxy bias, which is included with explicit redshift and multipole dependencies as b g ( z , ℓ ) = b g 0 ( 1 + z ) α ( ℓ / ℓ 0 ) β , with ℓ 0 = 117. We obtain the constraints to be B = 1.50 ± 0.07(stat) ± 0.34(sys), i.e., 1 − b H = 0.67 ± 0.03(stat) ± 0.16(sys) (68% confidence level) for the hydrostatic mass bias, and b g 0 = 1.28 − 0.04 + 0.03 ( stat ) ± 0.11 ( sys ) , with α = 0.20 − 0.07 + 0.11 ( stat ) ± 0.10 ( sys ) and β = 0.45 ±0.01(stat) ± 0.02(sys) for the galaxy bias. Incoming data sets from future CMB and galaxy surveys (e.g., Rubin Observatory) will allow probing the large-scale gas distribution in more detail.

Exploring the Mass and Redshift Dependencies of the Cluster Pressure Profile with Stacks on Thermal Sunyaev–Zel’dovich Maps

“…Our estimates are typically in the range from 0.2 to 0.3, with somewhat larger values obtained with ACT; the confidence intervals are in any case quite broad, with typical error bars up to ∼0.2, and no evident strong degeneracies with the other parameters. A number of different estimates for b h have been provided in the literature; for a summary, see, e.g., Table 3 in Ibitoye et al (2022). Our results are again in agreement with previous findings.…”

“…The numerical simulation results tend to agree that the hydrostatic bias has a mass dependence, and that it can accommodate values for b h as large as 0.3, for massive clusters (Pearce et al 2020;Barnes et al 2021). Our findings are also in agreement with crosscorrelation analyses (Makiya et al 2020;Rotti et al 2021;Ibitoye et al 2022) and studies of variously selected cluster samples (von der Linden et al 2014;Hoekstra et al 2015;Sereno et al 2017;Ferragamo et al 2021;Aguado-Barahona et al 2022). We stress, however, that our MCMC analysis does not provide strong constraints on the hydrostatic bias.…”

Mass bias and cosmological constraints from Planck cluster clustering

“…When comparing our results to other analyses based on cluster counts, we found a 1σ agreement with Zubeldia & Challinor (2019), Salvati et al (2019), andSalvati et al (2022). Concerning the results derived from the power spectra of the Planck thermal Sunyaev-Zeldovich effect, our constraint is in agreement within 1σ with Makiya et al (2018) and Ibitoye et al (2022). We also found a good agreement with the constraint by Wicker et al (2022), based on measurements of the cluster gas mass fraction.…”

Cross Correlation between the Thermal Sunyaev–Zeldovich Effect and the Integrated Sachs–Wolfe Effect

“…These foregrounds mainly dominate on small scales. For example, the correlated noise power spectrum may only match the yy spectrum amplitude at the multipoles ℓ ; 2742 (Ibitoye et al 2022). In our analysis, because of the resolution of the y-map N side = 64, the sensible ℓ-range is up to ℓ 192 max  (see Figure 3).…”

“…Ma et al (2021) used the tSZ-lensing cross correlation to constrain the pressure profile of galaxy clusters. A series of other studies based on cross correlating the tSZ results with other tracers to measure the cluster mass bias can be found in Ma (2017), Bolliet et al (2018), Li et al (2018), Makiya et al (2018), Salvati et al (2019), Koukoufilippas et al (2020), andIbitoye et al (2022).…”