English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

In this paper we explore the use of spatial clustering algorithms as a newcomputational approach for modeling the cosmic web. We demonstrate that suchalgorithms are efficient in terms of computing time needed. We explore threedistinct spatial methods which we suitably adjust for (i) detecting thetopology of the cosmic web and (ii) categorizing various cosmic structures asvoids, walls, clusters and superclusters based on a variety of topological andphysical criteria such as the physical distance between objects, their massesand local densities. The methods explored are (1) a new spatial method calledGravity Lattice ; (2) a modified version of another spatial clusteringalgorithm, the ABACUS; and (3) the well known spatial clustering algorithmHDBSCAN. We utilize HDBSCAN in order to detect cosmic structures and categorizethem using their overdensity. We demonstrate that the ABACUS method can becombined with the classic DTFE method to obtain similar results in terms of theachieved accuracy with about an order of magnitude less computation time. Tofurther solidify our claims, we draw insights from the computer science domainand compare the quality of the results with and without the application of ourmethod. Finally, we further extend our experiments and verify theireffectiveness by showing their ability to scale well with different cosmic webstructures that formed at different redshifts.

Detecting and analysing the topology of the cosmic web with spatial clustering algorithms I: Methods