Matter distribution in a cosmic perspective jeffrey bennett pdf section of the Universe. Universe, the anisotropies grew larger in scale over time. Voids located in high-density environments are smaller than voids situated in low-density spaces of the universe. The structure of our Universe can be broken down into components that can help describe the characteristics of individual regions of the cosmos.
Voids have a mean density less than a tenth of the average density of the universe. This serves as a working definition even though there is no single agreed-upon definition of what constitutes a void. Computer simulations sophisticated enough to provide relatively reliable results of growth and evolution of the large scale structure emerged and yielded insight on key features of the large scale galaxy distribution. Details of the supercluster and void structure of the Perseus-Pisces region were surveyed. The Center for Astrophysics Redshift Survey revealed that large voids, sharp filaments, and the walls that surround them dominate the large-scale structure of the Universe. Comparisons of optically selected galaxy surveys indicate that the same voids are found regardless of the sample selection. The completed two-degree Field Galaxy Redshift Survey adds a significantly large amount of voids to the database of all known cosmic voids.
There exist a number of ways for finding voids with the results of large-scale surveys of the Universe. Of the many different algorithms, virtually all fall into one of three general categories. The first class consists of void finders that try to find empty regions of space based on local galaxy density. The second class are those which try to find voids via the geometrical structures in the dark matter distribution as suggested by the galaxies. The third class is made up of those finders which identify structures dynamically by using gravitationally unstable points in the distribution of dark matter.
This first class method uses each galaxy in a catalog as its target and then uses the Nearest Neighbor Approximation to calculate the cosmic density in the region contained in a spherical radius determined by the distance to the third closest galaxy. Piran introduced this method in 1997 to allow a quick and effective method for standardizing the cataloging of voids. Once the spherical cells are mined from all of the structure data, each cell is expanded until the underdensity returns to average expected wall density values. The remaining walls and overlapping void regions are then gridded into respectively distinct and intertwining zones of filaments, clusters, and near-empty voids.
All voids admitted to the catalog had a minimum radius of 10 Mpc in order to ensure all identified voids were not accidentally cataloged due to sampling errors. This particular second class algorithm uses a Voronoi tessellation technique and mock border particles in order to categorize regions based on a high density contrasting border with a very low amount of bias. Neyrinck introduced this algorithm in 2008 with the purpose of introducing a method that did not contain free parameters or presumed shape tessellations. Therefore, this technique can create more accurately shaped and sized void regions.
Although this algorithm has some advantages in shape and size, it has been criticized often for sometimes providing loosely defined results. Since it has no free parameters, it mostly finds small and trivial voids, although the algorithm places a statistical significance on each void it finds. A physical significance parameter can be applied in order to reduce the number of trivial voids by including a minimum density to average density ratio of at least 1:5. Subvoids are also identified using this process which raises more philosophical questions on what qualifies as a void.