Detecting and analysing the topology of the cosmic web with spatial clustering algorithms I: Methods
Dimitrios Kelesis, Spyros Basilakos, Vicky Papadopoulou Lesta,, Dimitris Fotakis, Andreas Efstathiou

TL;DR
This paper introduces and evaluates spatial clustering algorithms, including a new method, for efficiently detecting and classifying the cosmic web's topology and structures across different cosmic epochs.
Contribution
It presents a novel spatial clustering approach, Gravity Lattice, and demonstrates its effectiveness alongside modifications to existing algorithms for cosmic web analysis.
Findings
Efficient detection of cosmic web topology using clustering algorithms.
Comparable accuracy achieved with reduced computational time.
Methods scale well across different cosmic structures and redshifts.
Abstract
In this paper we explore the use of spatial clustering algorithms as a new computational approach for modeling the cosmic web. We demonstrate that such algorithms are efficient in terms of computing time needed. We explore three distinct spatial methods which we suitably adjust for (i) detecting the topology of the cosmic web and (ii) categorizing various cosmic structures as voids, walls, clusters and superclusters based on a variety of topological and physical criteria such as the physical distance between objects, their masses and local densities. The methods explored are (1) a new spatial method called Gravity Lattice ; (2) a modified version of another spatial clustering algorithm, the ABACUS; and (3) the well known spatial clustering algorithm HDBSCAN. We utilize HDBSCAN in order to detect cosmic structures and categorize them using their overdensity. We demonstrate that the…
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.
Taxonomy
TopicsGalaxies: Formation, Evolution, Phenomena · Opportunistic and Delay-Tolerant Networks · Genome Rearrangement Algorithms
