相关论文: Finding cosmic voids and filament loops using topo…
Persistent homology studies the evolution of k-dimensional holes along a nested sequence of simplicial complexes (called a filtration). The set of bars (i.e. intervals) representing birth and death times of k-dimensional holes along such…
Cosmic voids are large, nearly empty regions that lie between the web of galaxies, filaments and walls, and are recognized for their extensive applications in the field of cosmology and astrophysics. Despite their significance, a universal…
Observations reveal that on large scales the universe is spanned by a percolating network of superclusters interspersed with large and almost empty regions -- voids. This thesis reports the construction of a sophisticated computational…
Topological data analysis is an emerging area in exploratory data analysis and data mining. Its main tool, persistent homology, has become a popular technique to study the structure of complex, high-dimensional data. In this paper, we…
The standard cosmological model, now strongly constrained by direct observation at early epochs, is very successful in describing the structure of the evolved universe on large and intermediate scales. Unfortunately, serious contradictions…
We present a method to numerically estimate the densities of a discretely sampled data based on binary space partitioning tree. We start with a root node containing all the particles and then recursively divide each node into two nodes each…
Void number counts (VNC) indicates the number of low-density regions in the large-scale structure (LSS) of the Universe, and we propose to use it as an effective cosmological probe. By generating the galaxy mock catalog based on Jiutian…
Persistent homology is an area within topological data analysis (TDA) that can uncover different dimensional holes (connected components, loops, voids, etc.) in data. The holes are characterized, in part, by how long they persist across…
The interstellar medium of galaxies is filled with holes, bubbles, and shells, typically interpreted as remnants of stellar evolution. There is growing interest in the study of their properties to investigate stellar and supernova feedback.…
An essential aspect of cosmic voids is that these underdense regions provide complementary information about the properties of our Universe. Unlike dense regions, voids are avoided by matter and are less contaminated by baryonic processes.…
The Large Scale Structure (LSS) found in galaxy redshift surveys and in computer simulations of cosmic structure formation shows a very complex network of galaxy clusters, filaments, and sheets around large voids. Here, we introduce a new…
Galaxy clusters and cosmic voids, the most extreme objects of our Universe in terms of mass and size, trace two opposite sides of the large-scale matter density field. By studying their abundance as a function of their mass and radius,…
A method is presented for the distributed computation of persistent homology, based on an extension of the generalized Mayer-Vietoris principle to filtered spaces. Cellular cosheaves and spectral sequences are used to compute global…
We develop a novel statistical strong lensing approach to probe the cosmological parameters by exploiting multiple redshift image systems behind galaxies or galaxy clusters. The method relies on free-form mass inversion of strong lenses and…
Topological Data Analysis (TDA) is a field that leverages tools and ideas from algebraic topology to provide robust methods for analysing geometric and topological aspects of data. One of the principal tools of TDA, persistent homology,…
We identify cosmic voids from galaxy density fields under the theory of void-cluster correspondence. We extend the previous novel void-identification method developed for the matter density field to the galaxy density field for practical…
Spatial transcriptomics studies are becoming increasingly large and commonplace, necessitating simultaneous analysis of a large number of spatially resolved variables. Correspondingly, a diverse range of methodologies have been proposed to…
We describe a new method (\textsc{CompaSO}) for identifying groups of particles in cosmological $N$-body simulations. \textsc{CompaSO} builds upon existing spherical overdensity (SO) algorithms by taking into consideration the tidal radius…
We demonstrate the capabilities of probabilistic diffusion models to reduce dramatically the computational cost of expensive hydrodynamical simulations to study the relationship between observable baryonic cosmological probes and dark…
We present a new method of finding cosmic voids using tomographic maps of Ly{\alpha} forest flux. We identify cosmological voids with radii of 2 - 12 $h^{-1}$Mpc in a large N-body simulation at $z = 2.5$, and characterize the signal of the…