The void-galaxy cross-correlation function with massive neutrinos and modified gravity

Abstract

Massive neutrinos and $f(R)$ modified gravity have degenerate observational signatures that can impact the interpretation of results in galaxy survey experiments, such as cosmological parameter estimations and gravity model tests. Because of this, it is important to investigate astrophysical observables that can break these degeneracies. Cosmic voids are sensitive to both massive neutrinos and modifications of gravity and provide a promising ground for disentangling the above mentioned degeneracies. In order to analyse cosmic voids in the context of non-$\Lambda$CDM cosmologies, we must first understand how well the current theoretical framework operates in these settings. We performed a suite of simulations with the RAMSES-based N-body code ANUBISIS, including massive neutrinos and $f(R)$ modified gravity both individually and simultaneously. The data from the simulations were compared to models of the void velocity profile and the void-halo cross-correlation function (CCF). This was done both with the real space simulation data as model input and by applying a reconstruction method to the redshift space data. In addition, we ran Markov chain Monte Carlo (MCMC) fits on the data sets to assess the capability of the models to reproduce the fiducial simulation values of $f{\sigma }_8(z)$ and the Alcock-Paczynski parameter, $\epsilon$. The void modelling applied performs similarly for all simulated cosmologies, indicating that more accurate models and higher resolution simulations are needed in order to directly observe the effects of massive neutrinos and $f(R)$ modified gravity through studies of the void-galaxy CCF. The MCMC fits show that the choice of void definition plays an important role in the recovery of the correct cosmological parameters, but otherwise, there is no clear distinction between the ability to reproduce $f{\sigma }_8$ and $\epsilon$ for the various simulations.

arXiv comment23 pages, 21 figures

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