Related papers: Hierarchical Clustering in ${\Lambda}$CDM Cosmolog…
The large-scale structure of the universe is comprised of virialized blob-like clusters, linear filaments, sheet-like walls and huge near empty three-dimensional voids. Characterizing the large scale universe is essential to our…
Building upon [2308.02636], we investigate the constraining power of persistent homology on cosmological parameters and primordial non-Gaussianity in a likelihood-free inference pipeline utilizing machine learning. We evaluate the ability…
We investigate the properties of the FLRW flat cosmological models in which the vacuum energy density evolves with time, $\Lambda(t)$. Using different versions of the $\Lambda(t)$ model, namely quantum field vacuum, power series vacuum and…
Inspired by the well-studied $\Lambda_{\rm s}$CDM model, we propose and investigate a class of dynamical dark energy models that evolve from a negative cosmological constant, transitioning to a positive value at low redshifts. Specifically,…
Galaxy clusters in the Universe occupy the important position of nodes of the cosmic web. They are connected among them by filaments, elongated structures composed of dark matter, galaxies, and gas. The connection of galaxy clusters to…
The mass function of cluster-size halos and their redshift distribution are computed for 12 distinct accelerating cosmological scenarios and confronted to the predictions of the conventional flat $\Lambda$CDM model. The comparison with…
Using cosmic voids to probe the growth rate of cosmic structure, and hence the nature of dark energy, is particularly interesting in the context of modified gravity theories that rely on the screening mechanism. In this work we improve the…
We report the {\em first} systematic study of the supercluster-void network in the $\Lambda$CDM concordance cosmology treating voids and superclusters on an equal footing. We study the dark matter density field in real space smoothed with…
We study the evolution of galaxy clustering in various cosmological models with quintessence. We investigate how the analytical predictions vary with change of dark energy equation of state $w_X$. Comparing these predictions against…
A large set of TREESPH simulations is used to test the global morphology of galaxy clusters and its evolution against X-ray data. A powerful method to investigate substructures in galaxy clusters are the power ratios introduced by Buote &…
In the context of a spatially flat $\Lambda(t)$CDM cosmology, we investigate interacting dark energy (IDE) scenarios characterized by phenomenological interaction terms proportional to the Hubble expansion rate and the dark energy density.…
In a previous paper, we proposed a heterotic dark energy model, called $\Lambda$HDE, in which dark energy is composed of two components: cosmological constant (CC) and holographic dark energy (HDE). The aim of this work is to give a more…
We study the dynamical properties of a large body of varying vacuum cosmologies for which dark matter interacts with vacuum. In particular, performing the critical point analysis we investigate the existence and the stability of…
Imagine a scenario in which the dark energy forms via the condensation of dark matter at some low redshift. The Compton wavelength therefore changes from small to very large at the transition, unlike quintessence or metamorphosis. We study…
We introduce a new technique for following the formation and evolution of galaxies in cosmological N-body simulations. Dissipationless simulations are used to track the formation and merging of dark matter halos as a function of redshift.…
This paper introduces a topological framework for interpreting the internal representations of Multilayer Perceptrons (MLPs). We construct a simplicial tower, a sequence of simplicial complexes connected by simplicial maps, that captures…
We investigate the formation, growth, merger history, movement, and destruction of cosmic voids detected via the watershed transform code VIDE in a cosmological N-body dark matter {\Lambda}CDM simulation. By adapting a method used to…
We discuss an analytic approach for modeling structure formation in sheets, filaments and knots. This is accomplished by combining models of triaxial collapse with the excursion set approach: sheets are defined as objects which have…
Persistent homology provides a robust methodology to infer topological structures from point cloud data. Here we explore the persistent homology of point clouds embedded into a probabilistic setting, exploiting the theory of point…
Convection is a well-studied topic in fluid dynamics, yet it is less understood in the context of networks flows. Here, we incorporate techniques from topological data analysis (namely, persistent homology) to automate the detection and…