The darkness that shaped the void: dark energy and cosmic voids
E. G. Patrick Bos (1), Rien van de Weygaert (1), Klaus Dolag (2, 3), and Valeria Pettorino (4) ((1) Kapteyn Astronomical Institute, University of, Groningen, (2) Department of Physics, Ludwig-Maximilians-Universit\"at, (3), Max Planck Institut f\"ur Astrophysik

TL;DR
This study investigates how the shapes of cosmic voids are influenced by dark energy and assesses their potential as observational probes in galaxy surveys, revealing significant sensitivity in dark matter but limitations in galaxy data.
Contribution
It demonstrates the sensitivity of void shapes to dark energy in simulations and evaluates the observational feasibility using galaxy surveys, highlighting the impact of clustering and bias.
Findings
Void shapes in dark matter are significantly sensitive to dark energy.
Clustering level _8(z) mainly causes differences in void shape.
Galaxy surveys face challenges due to sparsity and bias in detecting cosmological differences.
Abstract
Aims: We assess the sensitivity of void shapes to the nature of dark energy that was pointed out in recent studies. We investigate whether or not void shapes are useable as an observational probe in galaxy redshift surveys. We focus on the evolution of the mean void ellipticity and its underlying physical cause. Methods: We analyse the morphological properties of voids in five sets of cosmological N-body simulations, each with a different nature of dark energy. Comparing voids in the dark matter distribution to those in the halo population, we address the question of whether galaxy redshift surveys yield sufficiently accurate void morphologies. Voids are identified using the parameter free Watershed Void Finder. The effect of redshift distortions is investigated as well. Results: We confirm the statistically significant sensitivity of voids in the dark matter distribution. We identify…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
