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

ABSTRACT In this paper, we explore the use of spatial clustering algorithms as a new computational approach for modelling 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) de...

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Published in:	Monthly notices of the Royal Astronomical Society Vol. 516; no. 4; pp. 5110 - 5124
Main Authors:	Kelesis, Dimitrios, Basilakos, Spyros, Papadopoulou Lesta, Vicky, Fotakis, Dimitris, Efstathiou, Andreas
Format:	Journal Article
Language:	English
Published:	Oxford University Press 27.09.2022
Subjects:	galaxies: clusters: general methods: data analysis dark matter large-scale structure of Universe cosmology: theory
ISSN:	0035-8711, 1365-2966
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Abstract	ABSTRACT In this paper, we explore the use of spatial clustering algorithms as a new computational approach for modelling 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 abacus method can be combined with the classic dtfe method to obtain similar results in terms of the achieved accuracy with about an order of magnitude less computation time. To further solidify our claims, we draw insights from the computer science domain and compare the quality of the results with and without the application of our method. Finally, we further extend our experiments and verify their effectiveness by showing their ability to scale well with different cosmic web structures that formed at different redshifts.
AbstractList	In this paper, we explore the use of spatial clustering algorithms as a new computational approach for modelling 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 abacus method can be combined with the classic dtfe method to obtain similar results in terms of the achieved accuracy with about an order of magnitude less computation time. To further solidify our claims, we draw insights from the computer science domain and compare the quality of the results with and without the application of our method. Finally, we further extend our experiments and verify their effectiveness by showing their ability to scale well with different cosmic web structures that formed at different redshifts. ABSTRACT In this paper, we explore the use of spatial clustering algorithms as a new computational approach for modelling 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 abacus method can be combined with the classic dtfe method to obtain similar results in terms of the achieved accuracy with about an order of magnitude less computation time. To further solidify our claims, we draw insights from the computer science domain and compare the quality of the results with and without the application of our method. Finally, we further extend our experiments and verify their effectiveness by showing their ability to scale well with different cosmic web structures that formed at different redshifts.
Author	Papadopoulou Lesta, Vicky Fotakis, Dimitris Kelesis, Dimitrios Efstathiou, Andreas Basilakos, Spyros
Author_xml	– sequence: 1 givenname: Dimitrios surname: Kelesis fullname: Kelesis, Dimitrios – sequence: 2 givenname: Spyros surname: Basilakos fullname: Basilakos, Spyros – sequence: 3 givenname: Vicky surname: Papadopoulou Lesta fullname: Papadopoulou Lesta, Vicky – sequence: 4 givenname: Dimitris surname: Fotakis fullname: Fotakis, Dimitris – sequence: 5 givenname: Andreas orcidid: 0000-0002-2612-4840 surname: Efstathiou fullname: Efstathiou, Andreas email: a.efstathiou@euc.ac.cy
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Copyright	2022 The Author(s) Published by Oxford University Press on behalf of Royal Astronomical Society 2022
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Keywords	galaxies: clusters: general methods: data analysis dark matter large-scale structure of Universe cosmology: theory
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Snippet	ABSTRACT In this paper, we explore the use of spatial clustering algorithms as a new computational approach for modelling the cosmic web. We demonstrate that... In this paper, we explore the use of spatial clustering algorithms as a new computational approach for modelling the cosmic web. We demonstrate that such...
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Title	Detecting and analysing the topology of the cosmic web with spatial clustering algorithms I: methods
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