The imprint of cosmic voids from the DESI Legacy Survey DR9 LRGs in the Planck 2018 lensing map through spectroscopically calibrated mocks

TL;DR

This study measures the lensing imprint of cosmic voids from the DESI Legacy Survey on the CMB, finding strong agreement with $ mf ext{Lambda CDM}$ predictions and resolving previous tensions.

Contribution

It introduces spectroscopically calibrated mocks for precise void-lensing cross-correlation measurements, achieving the highest detection significance and confirming $ mf ext{Lambda CDM}$ consistency.

Findings

14σ detection of the lensing signal

Agreement with $ mf ext{Lambda CDM}$ predictions

Highlighting the importance of mock-observation matching

Abstract

The cross-correlation of cosmic voids with the lensing convergence ($\kappa$) map of the Cosmic Microwave Background (CMB) fluctuations provides a powerful tool to refine our understanding of the cosmological model. However, several studies have reported a moderate tension between the lensing imprint of cosmic voids on the observed CMB and the simulated $\mathrm{\Lambda}$CDM signal. To address this "lensing-is-low" tension and to obtain new, precise measurements, we exploit the large DESI Legacy Survey Luminous Red Galaxy (LRG) dataset, covering approximately 19,500 $\deg^2$ of the sky and including about 10 million LRGs at $z < 1.05$. Our $\mathrm{\Lambda}$CDM template was created using the Buzzard mocks, which we specifically calibrated to match the clustering properties of the observed galaxy sample by exploiting more than one million DESI spectra. We identified our catalogs of 3D voids in the range $0.35 < z < 0.95$, dividing the sample into bins according to the redshift and $\lambda_\mathrm{v}$ values of the voids. We report a 14$\sigma$ detection of the lensing signal, with $A_\kappa = 1.016 \pm 0.054$, which increases to 17$\sigma$ when considering the void-in-void ($A_\kappa = 0.944 \pm 0.064$) and the void-in-cloud ($A_\kappa = 0.975 \pm 0.060$) populations individually, the highest detection significance for studies of this kind. We observe a full agreement between the observations and $\mathrm{\Lambda}$CDM predictions across all redshift bins, sky regions, and void populations considered. In addition to these findings, our analysis highlights the importance of matching sparseness and redshift error distributions between mocks and observations, as well as the role of $\lambda_\mathrm{v}$ in enhancing the signal-to-noise ratio.