Weak-lensing tunnel voids in simulated light cones: A new pipeline to investigate modified gravity and massive neutrinos signatures

Abstract

Cosmic voids offer a unique opportunity to explore modified gravity (MG) models. Their low-density nature and vast extent make them especially sensitive to cosmological scenarios of the class $f(R)$, which incorporate screening mechanisms in dense, compact regions. Weak lensing (WL) by voids, in particular, provides a direct probe for testing MG scenarios. While traditional voids are identified from 3D galaxy positions, 2D voids detected in WL maps trace underdense regions along the line of sight and are sensitive to unbiased matter distribution. To investigate this, we developed an efficient pipeline for identifying and analyzing tunnel voids, i.e., underdensities detected in WL maps, specifically in the signal-to-noise ratio (SNR) of the convergence. In this work, we used this pipeline to generate realistic SNR maps from cosmological simulations featuring different $f(R)$ scenarios and massive neutrinos, comparing their effects against the standard $\Lambda$CDM model. Using the convergence maps and the 2D void catalogs, we analyzed various statistics, including the probability density function, angular power spectrum, and void size function. We then focused on the tangential shear profile around 2D voids, demonstrating how the proposed void-finding algorithm maximizes the signal. We showed that MG leads to deeper void shear profiles due to the enhanced evolution of cosmic structures, while massive neutrinos have the opposite effect. Furthermore, we found that parametric functions typically applied to 3D void density profiles are not suitable for deriving the shear profiles of tunnel voids. We, therefore, proposed a new parametric formula that provides an excellent fit to the void shear profiles across different void sizes and cosmological models. Finally, we tested the sensitivity of the free parameters of this new formula to the cosmological model.

22 pages, 16 figures

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