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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| Vydané v: | Monthly notices of the Royal Astronomical Society Ročník 516; číslo 4; s. 5110 - 5124 |
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| Hlavní autori: | , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Oxford University Press
27.09.2022
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| 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. |
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| AbstractList | 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. 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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| Cites_doi | 10.1093/mnras/sty1643 10.1111/j.1468-4004.2009.50512.x 10.1146/annurev-statistics-031017-100045 10.1086/311214 10.1086/172975 10.1051/0004-6361/201321489 10.1093/mnras/stw2862 10.1093/mnrasl/slx008 10.1093/mnras/stab2326 10.1088/1475-7516/2021/04/061 10.1093/mnras/stw803 10.1093/mnras/sty618 10.1093/mnras/stw2944 10.1007/978-3-642-25249-5_3 10.1046/j.1365-8711.2001.04902.x 10.1038/nature13316 10.1046/j.1365-8711.2001.04226.x 10.1017/S1743921316010504 10.1088/0004-637X/723/1/364 10.1088/1475-7516/2019/09/052 10.1051/0004-6361/201525830 10.1093/mnras/stx2656 10.1051/aas:1997340 10.1038/300407a0 10.1111/j.1365-2966.2011.18394.x 10.1137/1.9781611972818.26 10.1007/978-1-4612-1098-6 10.1201/9781420073980 10.1086/301513 10.1093/mnras/stx3040 10.1093/mnras/sty2206 10.1111/j.1365-2966.2004.08060.x 10.1186/s40668-019-0028-x 10.1007/s00454-002-2885-2 10.1111/j.1365-2966.2005.09731.x 10.1086/162806 10.1088/0067-0049/206/1/3 10.1093/mnras/stx3304 10.1111/j.1365-2966.2008.13307.x 10.1109/ICPR.2010.579 10.1093/mnras/stx1976 10.1111/j.1365-2966.2009.15715.x 10.1093/mnras/stv722 10.5303/JKAS.2013.46.3.125 10.1086/382125 10.1046/j.1365-8711.2003.06845.x 10.1046/j.1365-8711.2003.06642.x 10.1093/mnras/stu768 10.1086/310492 10.1016/j.ascom.2015.09.003 10.1007/978-1-4020-6941-3_11 10.1109/TIP.2003.819861 10.1038/380603a0 10.1093/mnras/stz541 10.1186/s40668-018-0026-4 10.1111/j.1365-2966.2010.17263.x |
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| Issue | 4 |
| Keywords | galaxies: clusters: general methods: data analysis dark matter large-scale structure of Universe cosmology: theory |
| Language | English |
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| References | Springel (2022112117242238200_bib55) 2010; 401 Aragón-Calvo (2022112117242238200_bib2) 2010; 723 Wilding (2022112117242238200_bib66) 2021; 507 Colless (2022112117242238200_bib14) 2001; 328 Klypin (2022112117242238200_bib28) 1993; 413 Barabási (2022112117242238200_bib3) 2016 Edelsbrunner (2022112117242238200_bib19) 2002; 28 Rodríguez (2022112117242238200_bib46) 2018; 5 Schaap (2022112117242238200_bib49) 2000; 363 Libeskind (2022112117242238200_bib30) 2017; 473 Nelson (2022112117242238200_bib69_1663521486375) 2019; 6 Scoville (2022112117242238200_bib51) 2013; 206 Sheth (2022112117242238200_bib53) 2003; 343 Vogelsberger (2022112117242238200_bib62) 2014; 509 Feldbrugge (2022112117242238200_bib21) 2019; 2019 Park (2022112117242238200_bib39) 2013; 46 Pranav (2022112117242238200_bib43) 2017; 465 Van de Weygaert (2022112117242238200_bib59) 2008 Preparata (2022112117242238200_bib45) 1985 Springel (2022112117242238200_bib56) 2018; 475 Wasserman (2022112117242238200_bib64) 2018; 5 Colberg (2022112117242238200_bib13) 2008; 387 Van de Weygaert (2022112117242238200_bib60) 2008 Marinacci (2022112117242238200_bib33) 2018; 480 Pillepich (2022112117242238200_bib40) 2018; 473 Pranav (2022112117242238200_bib44) 2019; 485 Cautun (2022112117242238200_bib10) 2014; 441 Schawinski (2022112117242238200_bib50) 2017; 467 Sousbie (2022112117242238200_bib54) 2011; 414 Driver (2022112117242238200_bib17) 2009; 50 Dekel (2022112117242238200_bib16) 1985; 288 Codis (2022112117242238200_bib12) 2018; 479 Planck Collaboration XIII (2022112117242238200_bib41) 2016; 594 Kingma (2022112117242238200_bib27) 2014 Edelsbrunner (2022112117242238200_bib18) 2010 Van de Weygaert (2022112117242238200_bib58) 2014; 11 Coutinho (2022112117242238200_bib15) 2016 Weinberger (2022112117242238200_bib65) 2017; 465 Goodfellow (2022112117242238200_bib22) 2014 York (2022112117242238200_bib67) 2000; 120 Hong (2022112117242238200_bib24) 2015; 450 Basilakos (2022112117242238200_bib4) 2003; 344 Kono (2022112117242238200_bib29) 2020 Horé (2022112117242238200_bib26) 2010 Malzer (2022112117242238200_bib31) 2020 Bond (2022112117242238200_bib8) 1996; 380 Sahni (2022112117242238200_bib47) 1997; 476 Guzzo (2022112117242238200_bib23) 2014; 566 Basilakos (2022112117242238200_bib5) 2001; 323 Einasto (2022112117242238200_bib20) 1997; 123 Miller (2022112117242238200_bib34) 2009 Naiman (2022112117242238200_bib35) 2018; 477 Nevenzeel (2022112117242238200_bib38) 2013 Chaoji (2022112117242238200_bib11) 2011 Hong (2022112117242238200_bib25) 2016; 459 Tegmark (2022112117242238200_bib57) 2004; 606 Van de Weygaert (2022112117242238200_bib61) 2011 Mandelbrot (2022112117242238200_bib32) 1975; 280 Aragón-Calvo (2022112117242238200_bib1) 2010; 408 Nelson (2022112117242238200_bib37) 2018; 475 Sahni (2022112117242238200_bib48) 1998; 495 Cautun (2022112117242238200_bib9) 2019 Basilakos (2022112117242238200_bib6) 2006; 365 Wang (2022112117242238200_bib63) 2004; 13 Nelson (2022112117242238200_bib36) 2015; 13 Shandarin (2022112117242238200_bib52) 2004; 353 Biagetti (2022112117242238200_bib7) 2021; 2021 Zeldovich (2022112117242238200_bib68) 1982; 300 |
| References_xml | – volume-title: A Study on the Baryon Acoustic Oscillation with Topological Data Analysis year: 2020 ident: 2022112117242238200_bib29 – volume: 479 start-page: 973 year: 2018 ident: 2022112117242238200_bib12 publication-title: MNRAS doi: 10.1093/mnras/sty1643 – volume: 50 start-page: 5.12 year: 2009 ident: 2022112117242238200_bib17 publication-title: Astron. Geophys. doi: 10.1111/j.1468-4004.2009.50512.x – volume: 5 start-page: 501 year: 2018 ident: 2022112117242238200_bib64 publication-title: Annu. Rev. Stat. Appl. doi: 10.1146/annurev-statistics-031017-100045 – volume: 495 start-page: L5 year: 1998 ident: 2022112117242238200_bib48 publication-title: ApJ doi: 10.1086/311214 – volume: 413 start-page: 48 year: 1993 ident: 2022112117242238200_bib28 publication-title: ApJ doi: 10.1086/172975 – volume: 566 start-page: A108 year: 2014 ident: 2022112117242238200_bib23 publication-title: A&A doi: 10.1051/0004-6361/201321489 – volume: 465 start-page: 4281 year: 2017 ident: 2022112117242238200_bib43 publication-title: MNRAS doi: 10.1093/mnras/stw2862 – volume: 467 start-page: L110 year: 2017 ident: 2022112117242238200_bib50 publication-title: MNRAS doi: 10.1093/mnrasl/slx008 – volume: 507 start-page: 2968 year: 2021 ident: 2022112117242238200_bib66 publication-title: MNRAS doi: 10.1093/mnras/stab2326 – volume-title: 2nd International Conference on Learning Representations, ICLR 2014 - Conference Track Proceedings year: 2014 ident: 2022112117242238200_bib27 – volume: 2021 start-page: 061 year: 2021 ident: 2022112117242238200_bib7 publication-title: J.Cosmology Astropart. Phys. doi: 10.1088/1475-7516/2021/04/061 – volume: 459 start-page: 2690 year: 2016 ident: 2022112117242238200_bib25 publication-title: MNRAS doi: 10.1093/mnras/stw803 – volume-title: The Network Behind the Cosmic Web year: 2016 ident: 2022112117242238200_bib15 – volume: 477 start-page: 1206 year: 2018 ident: 2022112117242238200_bib35 publication-title: MNRAS doi: 10.1093/mnras/sty618 – volume: 465 start-page: 3291 year: 2017 ident: 2022112117242238200_bib65 publication-title: MNRAS doi: 10.1093/mnras/stw2944 – start-page: 60 volume-title: Alpha, Betti and the Megaparsec Universe: On the Topology of the Cosmic Web year: 2011 ident: 2022112117242238200_bib61 doi: 10.1007/978-3-642-25249-5_3 – volume: 328 start-page: 1039 year: 2001 ident: 2022112117242238200_bib14 publication-title: MNRAS doi: 10.1046/j.1365-8711.2001.04902.x – volume: 509 start-page: 177 year: 2014 ident: 2022112117242238200_bib62 publication-title: Nature doi: 10.1038/nature13316 – volume: 323 start-page: 47 year: 2001 ident: 2022112117242238200_bib5 publication-title: MNRAS doi: 10.1046/j.1365-8711.2001.04226.x – volume: 280 start-page: 1551 year: 1975 ident: 2022112117242238200_bib32 publication-title: Academie des Sciences Paris Comptes Rendus Serie Sciences Mathematiques – volume: 11 start-page: 493 year: 2014 ident: 2022112117242238200_bib58 publication-title: Proc. Int. Astron. Union doi: 10.1017/S1743921316010504 – volume: 723 start-page: 364 year: 2010 ident: 2022112117242238200_bib2 publication-title: ApJ doi: 10.1088/0004-637X/723/1/364 – volume: 2019 start-page: 052 year: 2019 ident: 2022112117242238200_bib21 publication-title: J. Cosmology Astropart. Phys. doi: 10.1088/1475-7516/2019/09/052 – volume: 594 start-page: A13 year: 2016 ident: 2022112117242238200_bib41 publication-title: A&A doi: 10.1051/0004-6361/201525830 – volume: 473 start-page: 4077 year: 2018 ident: 2022112117242238200_bib40 publication-title: MNRAS doi: 10.1093/mnras/stx2656 – volume: 123 start-page: 119 year: 1997 ident: 2022112117242238200_bib20 publication-title: A&AS doi: 10.1051/aas:1997340 – volume: 300 start-page: 407 year: 1982 ident: 2022112117242238200_bib68 publication-title: Nature doi: 10.1038/300407a0 – volume: 414 start-page: 350 year: 2011 ident: 2022112117242238200_bib54 publication-title: MNRAS doi: 10.1111/j.1365-2966.2011.18394.x – start-page: 295 volume-title: in Proceedings of the Eleventh SIAM International Conference on Data Mining, SDM 2011, April 28-30, 2011, SIAM / Omnipress year: 2011 ident: 2022112117242238200_bib11 doi: 10.1137/1.9781611972818.26 – volume-title: Computational Geometry: An Introduction year: 1985 ident: 2022112117242238200_bib45 doi: 10.1007/978-1-4612-1098-6 – volume-title: Geographic Data Mining and Knowledge Discovery year: 2009 ident: 2022112117242238200_bib34 doi: 10.1201/9781420073980 – volume: 120 start-page: 1579 year: 2000 ident: 2022112117242238200_bib67 publication-title: AJ doi: 10.1086/301513 – start-page: 291 volume-title: Lecture Notes in Physics, vol. 665 year: 2008 ident: 2022112117242238200_bib60 – volume: 475 start-page: 624 year: 2018 ident: 2022112117242238200_bib37 publication-title: MNRAS doi: 10.1093/mnras/stx3040 – volume: 480 start-page: 5113 year: 2018 ident: 2022112117242238200_bib33 publication-title: MNRAS doi: 10.1093/mnras/sty2206 – volume: 353 start-page: 162 year: 2004 ident: 2022112117242238200_bib52 publication-title: MNRAS doi: 10.1111/j.1365-2966.2004.08060.x – volume: 6 start-page: 1 year: 2019 ident: 2022112117242238200_bib69_1663521486375 publication-title: Computational Astrophysics and Cosmology doi: 10.1186/s40668-019-0028-x – volume-title: Computational Topology: An Introduction year: 2010 ident: 2022112117242238200_bib18 – volume: 28 start-page: 511 year: 2002 ident: 2022112117242238200_bib19 publication-title: Discrete & Computational Geometry doi: 10.1007/s00454-002-2885-2 – volume: 365 start-page: 539 year: 2006 ident: 2022112117242238200_bib6 publication-title: MNRAS doi: 10.1111/j.1365-2966.2005.09731.x – volume: 288 start-page: 411 year: 1985 ident: 2022112117242238200_bib16 publication-title: ApJ doi: 10.1086/162806 – start-page: 223 volume-title: IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) year: 2020 ident: 2022112117242238200_bib31 – volume: 206 start-page: 3 year: 2013 ident: 2022112117242238200_bib51 publication-title: ApJS doi: 10.1088/0067-0049/206/1/3 – volume: 475 start-page: 676 year: 2018 ident: 2022112117242238200_bib56 publication-title: MNRAS doi: 10.1093/mnras/stx3304 – volume-title: The DTFE public software: The Delaunay Tessellation Field Estimator code year: 2019 ident: 2022112117242238200_bib9 – volume: 387 start-page: 933 year: 2008 ident: 2022112117242238200_bib13 publication-title: MNRAS doi: 10.1111/j.1365-2966.2008.13307.x – start-page: 2366 volume-title: in 20th International Conference on Pattern Recognition, ICPR 2010, 23-26 August 2010 year: 2010 ident: 2022112117242238200_bib26 doi: 10.1109/ICPR.2010.579 – volume: 473 start-page: 1195 year: 2017 ident: 2022112117242238200_bib30 publication-title: MNRAS doi: 10.1093/mnras/stx1976 – volume-title: Network Science year: 2016 ident: 2022112117242238200_bib3 – volume: 401 start-page: 791 year: 2010 ident: 2022112117242238200_bib55 publication-title: MNRAS doi: 10.1111/j.1365-2966.2009.15715.x – start-page: 2672 volume-title: Advances in Neural Information Processing Systems year: 2014 ident: 2022112117242238200_bib22 – volume: 450 start-page: 1999 year: 2015 ident: 2022112117242238200_bib24 publication-title: MNRAS doi: 10.1093/mnras/stv722 – volume: 46 start-page: 125 year: 2013 ident: 2022112117242238200_bib39 publication-title: J. Korean Astron. Soc. doi: 10.5303/JKAS.2013.46.3.125 – volume: 363 start-page: L29 year: 2000 ident: 2022112117242238200_bib49 publication-title: A&A – volume: 606 start-page: 702 year: 2004 ident: 2022112117242238200_bib57 publication-title: ApJ doi: 10.1086/382125 – volume: 344 start-page: 602 year: 2003 ident: 2022112117242238200_bib4 publication-title: MNRAS doi: 10.1046/j.1365-8711.2003.06845.x – volume: 343 start-page: 22 year: 2003 ident: 2022112117242238200_bib53 publication-title: MNRAS doi: 10.1046/j.1365-8711.2003.06642.x – volume: 441 start-page: 2923 year: 2014 ident: 2022112117242238200_bib10 publication-title: MNRAS doi: 10.1093/mnras/stu768 – volume: 476 start-page: L1 year: 1997 ident: 2022112117242238200_bib47 publication-title: ApJ doi: 10.1086/310492 – volume: 13 start-page: 12 year: 2015 ident: 2022112117242238200_bib36 publication-title: Astron. Comput. doi: 10.1016/j.ascom.2015.09.003 – volume-title: Triangulating the Darkness: Topological Dark Energy Differentiation year: 2013 ident: 2022112117242238200_bib38 – start-page: 409 volume-title: Observations and Morphology of the Cosmic Web year: 2008 ident: 2022112117242238200_bib59 doi: 10.1007/978-1-4020-6941-3_11 – volume: 13 start-page: 600 year: 2004 ident: 2022112117242238200_bib63 publication-title: IEEE Trans. Image Process. doi: 10.1109/TIP.2003.819861 – volume: 380 start-page: 603 year: 1996 ident: 2022112117242238200_bib8 publication-title: Nature doi: 10.1038/380603a0 – volume: 485 start-page: 4167 year: 2019 ident: 2022112117242238200_bib44 publication-title: MNRAS doi: 10.1093/mnras/stz541 – volume: 5 start-page: 4 year: 2018 ident: 2022112117242238200_bib46 publication-title: Comput. Astrophys doi: 10.1186/s40668-018-0026-4 – volume: 408 start-page: 2163 year: 2010 ident: 2022112117242238200_bib1 publication-title: MNRAS doi: 10.1111/j.1365-2966.2010.17263.x |
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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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