Hostname: page-component-857557d7f7-c8jtx Total loading time: 0 Render date: 2025-12-01T17:54:39.678Z Has data issue: false hasContentIssue false
  • English
  • Français

Nucleation regions in the Large-Scale Structure I: A catalogue of cores in nearby rich superclusters

Published online by Cambridge University Press:  03 June 2024

Johan M. Zúñiga Open the ORCID record for Johan M. Zúñiga [Opens in a new window] ,
César A. Caretta Open the ORCID record for César A. Caretta [Opens in a new window]  and
Heinz Andernach Open the ORCID record for Heinz Andernach [Opens in a new window]
Johan M. Zúñiga*
Affiliation:
Departamento de Astronomía, DCNE-CGT, Universidad de Guanajuato, Guanajuato, GTO, Mexico
César A. Caretta
Affiliation:
Departamento de Astronomía, DCNE-CGT, Universidad de Guanajuato, Guanajuato, GTO, Mexico
Heinz Andernach
Affiliation:
Departamento de Astronomía, DCNE-CGT, Universidad de Guanajuato, Guanajuato, GTO, Mexico Currently on leave at Thüringer Landessternwarte, Tautenburg, Germany
*
Corresponding author: Johan M. Zúñiga; Email: jm.zuniga@ugto.mx
Get access Rights & Permissions [Opens in a new window]

Abstract

We applied a Density-Based Clustering algorithm on samples of galaxies and galaxy systems belonging to 53 rich superclusters from the Main SuperCluster Catalogue to identify the presence of “central regions’’, or cores, in these large-scale structures. Cores are defined here as large gravitationally bound galaxy structures, comprised of two or more clusters and groups, with sufficient matter density to survive cosmic expansion and virialize in the future. We identified a total of 105 galaxy structures classified as cores, which exhibit a high density contrast of mass and galaxies. The Density-based Core Catalogue, presented here, includes cores that were previously reported in well-known superclusters of the Local Universe, and also several newly identified ones. We found that 83% of the rich superclusters in our sample have at least one core. While more than three cores with different dynamical state are possible, the presence of a single core in the superclusters is more common. Our work confirms the existence of nucleation regions in the internal structure of most rich superclusters and points to the fact that these cores are the densest and most massive features that can be identified in the cosmic web with high probability for future virialization.

Keywords

Large-scale structure of Universegalaxies:clusters:generalcataloguesgalaxies:groups:general

Information

Type
Research Article
Information
Publications of the Astronomical Society of Australia , Volume 41 , 2024 , e078
Check for updates
Copyright
© The Author(s), 2024. Published by Cambridge University Press on behalf of Astronomical Society of Australia

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

Abdullah, M. H., Wilson, G., Klypin, A., Old, L., Praton, E., & Ali, G. B. 2020, ApJS, 246, 210.3847/1538-4365/ab536eCrossRefGoogle Scholar
Abell, G. O. 1958, ApJS, 3, 21110.1086/190036CrossRefGoogle Scholar
Abell, G. O. 1961, AJ, 66, 60710.1086/108472CrossRefGoogle Scholar
Abell, G. O., Corwin, H. Jr. G., & Olowin, R. P. 1989, ApJS, 70, 1CrossRefGoogle Scholar
Albareti, F. D., et al. 2017, ApJS, 233, 25Google Scholar
Andernach, H., Tago, E., Einasto, M., Einasto, J., & Jaaniste, J. 2005, in ASP Conference Series, ed. Fairall, A. P., & Woudt, P. A. (San Francisco: Astronomical Society of the Pacific), 329, 283 Google Scholar
Araya-Melo, P. A., Reisenegger, A., Meza, A., van de Weygaert, R., Dünner, R., & Quintana, H. 2009, MNRAS, 399, 9710.1111/j.1365-2966.2009.15292.xCrossRefGoogle Scholar
Bahcall, N. A. 1996, Preprint (arXiv:astro-ph/9611148v1).Google Scholar
Bahcall, N. A., & Soneira, R. M. 1984, ApJ, 277, 2710.1086/161667CrossRefGoogle Scholar
Bardelli, S., et al. 1994, MNRAS, 267, 665CrossRefGoogle Scholar
Bardelli, S., Zucca, E., Zamorani, G., Moscardini, L., & Scaramella, R. 2000, MNRAS, 312, 54010.1046/j.1365-8711.2000.03174.xCrossRefGoogle Scholar
Beers, T. C., Flynn, K., & Gebhardt, K. 1990, AJ, 100, 3210.1086/115487CrossRefGoogle Scholar
Beers, T. C., Geller, M. J., & Huchra, J. P. 1982, ApJ, 257, 2310.1086/159958CrossRefGoogle Scholar
Berlind, A. A., et al. 2006, ApJS, 167, 1Google Scholar
Biviano, A., Murante, G., Borgani, S., Diaferio, A., Dolag, K., & Girardi, M. 2006, A&A, 456, 2310.1051/0004-6361:20064918CrossRefGoogle Scholar
Böhringer, H., & Chon, G. 2021, A&A, 656, A144 CrossRefGoogle Scholar
Böhringer, H., Chon, G., & Trümper, J. 2021, A&A, 651, A16 10.1051/0004-6361/202140864CrossRefGoogle Scholar
Bolton, A. S., et al. 2012, AJ, 144, 144Google Scholar
Breen, J., Raychaudhury, S., Forman, W., & Jones, C. 1994, ApJ, 424, 59CrossRefGoogle Scholar
Bryan, G. L., & Norman, M. L. 1998, ApJ, 495, 8010.1086/305262CrossRefGoogle Scholar
Caretta, C. A., Andernach, H., Chow-Martínez, M., Coziol, R., De Anda-Suárez, J., Hernández-Aguayo C., & et al. 2023, RMxAA, 59, 345 10.22201/ia.01851101p.2023.59.02.13CrossRefGoogle Scholar
Caretta, C. A., Maia, M. A., Kawasaki, W., & Willmer, C. N. A. 2002, AJ, 123, 120010.1086/338894CrossRefGoogle Scholar
Chiueh, T., & He, X.-G. 2002, PhRvD, 65, 12351810.1103/PhysRevD.65.123518CrossRefGoogle Scholar
Chon, G., Böhringer, H., & Nowak, N. 2013, MNRAS, 429, 3272CrossRefGoogle Scholar
Chon, G., Böhringer, H., & Zaroubi, S. 2015, A&A, 575, L14 10.1051/0004-6361/201425591CrossRefGoogle Scholar
Chow-Martínez, M. 2019, Doctoral thesis, Universidad de Guanajuato, MexicoGoogle Scholar
Chow-Martínez, M., Andernach, H., Caretta, C. A., & Trejo-Alonso, J. J. 2014, MNRAS, 445, 407310.1093/mnras/stu1961CrossRefGoogle Scholar
Coil, A. L. 2012, Large scale structure of the universe. Preprint (arXiv:1202.6633v1)Google Scholar
Cole, S., et al. (2dFGRS team) 2005, MNRAS, 362, 50510.1111/j.1365-2966.2005.09318.xCrossRefGoogle Scholar
Colless, M., et al. (the 2dFGRS team) 2001, MNRAS, 328, 103910.1046/j.1365-8711.2001.04902.xCrossRefGoogle Scholar
Costa-Duarte, M. V., Sodré, L. Jr., & Durret, F. 2011, MNRAS, 411, 171610.1111/j.1365-2966.2010.17803.xCrossRefGoogle Scholar
Davis, M., Efstathiou, G., Frenk, C. S., & White, S. D. M. 1985, ApJ, 292, 37110.1086/163168CrossRefGoogle Scholar
Dünner, R., Araya, P. A., Meza, A., & Reisenegger, A. 2006, MNRAS, 366, 80310.1111/j.1365-2966.2005.09955.xCrossRefGoogle Scholar
Edelsbrunner, H., & Mücke, E. P. 1994, ACM TG, 13, 4310.1145/174462.156635CrossRefGoogle Scholar
Einasto, J. 2010, AIP Conference Proceedings, Vol. 1205, 7210.1063/1.3382336CrossRefGoogle Scholar
Einasto, J., et al. 2007, A&A, 462, 81110.1051/0004-6361:20065296CrossRefGoogle Scholar
Einasto, J., Klypin, A. A., Saar, E., & Shandarin, S. F. 1984, MNRAS, 206, 529CrossRefGoogle Scholar
Einasto, J., Suhhonenko, I., Liivamägi, L. J., & Einasto, M. 2018, A&A, 616, A141 10.1051/0004-6361/201833011CrossRefGoogle Scholar
Einasto, J., Suhhonenko, I., Liivamägi, L. J., & Einasto, M. 2019, A&A, 623, A97 10.1051/0004-6361/201834450CrossRefGoogle Scholar
Einasto, M., Einasto, J., Tago, E., Müller, V., & Andernach, H. 2001, AJ, 122, 2222CrossRefGoogle Scholar
Einasto, M., et al. 2007, A&A, 464, 815CrossRefGoogle Scholar
Einasto, M., et al. 2024, A&A, 681, A91 10.1051/0004-6361/202347504CrossRefGoogle Scholar
Einasto, M., et al. 2015, A&A, 580, A69 10.1051/0004-6361/201526399CrossRefGoogle Scholar
Einasto, M., et al. 2021, A&A, 649, A51 10.1051/0004-6361/202040200CrossRefGoogle Scholar
Einasto, M., et al. 2016, A&A, 595, A70 CrossRefGoogle Scholar
Einasto, M., et al. 2007, A&A, 476, 69710.1051/0004-6361:20078037CrossRefGoogle Scholar
Einasto, M., et al. 2008, ApJ, 685, 8310.1086/590374CrossRefGoogle Scholar
Ester, M., Kriegel, H. P., Sander, J., & Xu, X. 1996, in Kdd-96 Proceedings, 226Google Scholar
Gal, R. R., et al. 2003, AJ, 125, 206410.1086/368240CrossRefGoogle Scholar
Gramann, M., & Suhhonenko, I. 2002, MNRAS, 337, 141710.1046/j.1365-8711.2002.06016.xCrossRefGoogle Scholar
Hambly, N. C., et al. 2001, MNRAS, 326, 127910.1111/j.1365-2966.2001.04660.xCrossRefGoogle Scholar
Heinämäki, P., Teerikorpi, P., Douspis, M., Nurmi, P., Einasto, M. ang Gramann, M., Nevalainen, J., & Saar, E. 2022, A&A, 668, A3710.1051/0004-6361/202244239CrossRefGoogle Scholar
Hogg, D. W. 2000, Preprint (arXiv:astro-ph/9905116v4)Google Scholar
Huchra, J. P., & Geller, M. J. 1982, ApJ, 257, 42310.1086/160000CrossRefGoogle Scholar
Jones, D. H., et al. 2009, MNRAS, 399, 683Google Scholar
Koester, B. P., et al. 2007, ApJ, 660, 239CrossRefGoogle Scholar
Kopylova, F. G., & Kopylov, A. I. 1998, AstL, 24, 491 Google Scholar
Kriegel, H. P., Kröger, P., Sander, J., & Zimek, A. 2011, Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 1, 231. https://doi.org/10.1002/widm.30 CrossRefGoogle Scholar
Libeskind, N. I., et al. 2018, MNRAS, 473, 1195 Google Scholar
Liivamägi, L. J., Tempel, E., & Saar, E. 2012, A&A, 539, A80 10.1051/0004-6361/201016288CrossRefGoogle Scholar
Liu, T., et al. 2015, ApJS, 216, 2810.1088/0067-0049/216/2/28CrossRefGoogle Scholar
Łokas, E. L., & Hoffman, Y. 2002, Preprint (astro/ph0108283)Google Scholar
Lumsden, S. L., Nichol, R. C., Collins, C. A., & Guzzo, L. 1992, MNRAS, 258, 110.1093/mnras/258.1.1CrossRefGoogle Scholar
Luparello, H., Lares, M., Lambas, D. G., & Padilla, N. 2011, MNRAS, 415, 96410.1111/j.1365-2966.2011.18794.xCrossRefGoogle Scholar
Marini, F., et al. 2004, MNRAS, 353, 121910.1111/j.1365-2966.2004.08148.xCrossRefGoogle Scholar
MATLAB. 2023, Matlab documentation: mathematics and optimization. Available in: http://www.mathworks.com/help/index.html The MathWorks, Inc.Google Scholar
McConnachie, A. W., Patton, D. R., Ellison, S. L., & Simard, L. 2009, MNRAS, 395, 25510.1111/j.1365-2966.2008.14340.xCrossRefGoogle Scholar
Melott, A. L., et al. 1983, PhRvL, 51, 93510.1103/PhysRevLett.51.935CrossRefGoogle Scholar
Miller, C. J., et al. 2005, AJ, 130, 96810.1086/431357CrossRefGoogle Scholar
Nakamura, O., et al. 2003, AJ, 125, 168210.1086/368135CrossRefGoogle Scholar
Ochsenbein, F., Bauer, P., & Marcout, J. 2000, A&AS, 143, 2310.1051/aas:2000169CrossRefGoogle Scholar
Oort, J. H. 1983, A&A, 21, 37310.1146/annurev.aa.21.090183.002105CrossRefGoogle Scholar
Pearson, D. W., Batiste, M., & Batuski, D. 2014, MNRAS, 441, 1601CrossRefGoogle Scholar
Peebles, P. J. E. 1980, The Large-Scale Structure of the Universe (Princeton, NJ: Princeton University Press)Google Scholar
Peñaranda-Rivera, J. D., Paipa-León, D. L., Hernández-Charpak, S. D., & Forero-Romero, J. E. 2021, MNRAS, 500, L32 CrossRefGoogle Scholar
Piffaretti, R., Arnaud, M., Pratt, G. W., Pointecouteau, E., & Melin, J.-B. 2011, A&A, 534, A109 Google Scholar
Quintana, H., Carrasco, E. R., & Reisenegger, A. 2000, AJ, 120, 511 CrossRefGoogle Scholar
Ragone, C. J., et al. 2006, A&A, 445, 819 Google Scholar
Ramella, M., Geller, M., Pisani, A., & Da Costa, L. 2002, AJ, 123, 2976CrossRefGoogle Scholar
Sander, J. 2011, in Encyclopedia of Machine Learning, ed. Sammut, C., & Webb, G. I. (Boston, MA: Springer). https://doi.org/10.1007/978-0-387-30164-8_211 Google Scholar
Sankhyayan, S., et al. 2023, ApJ, 958, 6210.3847/1538-4357/acfaebCrossRefGoogle Scholar
Santiago-Bautista, I., Caretta, C. A., Bravo-Alfaro, H., Pointecouteau, E., & Andernach, H. 2020, A&A, 637, A31 10.1051/0004-6361/201936397CrossRefGoogle Scholar
Sargent, W. L. W., & Turner, E. L. 1977, ApJ, 212, L3 10.1086/182362CrossRefGoogle Scholar
Schneider, P. 2015, Extragalactic Astronomy and Cosmology: An Introduction. Second (Springer)CrossRefGoogle Scholar
Serna, A., & Gerbal, D. 1996, A&A, 309, 65Google Scholar
Shandarin, S. F. 1983, SvAL, 9, 104CrossRefGoogle Scholar
Shandarin, S. F., Sheth, J. V., & Sahni, V. 2004, MNRAS, 353, 162CrossRefGoogle Scholar
Sheth, R., & Diaferio, A. 2011, MNRAS, 417, 4CrossRefGoogle Scholar
Small, T. A., Ma, C. P., Sargent, W. L. W., & Hamilton, D. 1998, ApJ, 492, 45 10.1086/305037CrossRefGoogle Scholar
Small, T. A., Sargent, W. L. W., & Hamilton, D. 1997, ApJS, 111, 110.1086/313009CrossRefGoogle Scholar
Smith, A. G., Hopkins, A. M., Hunstead, R. W., & Pimbblet, K. A. 2012, MNRAS, 422, 25CrossRefGoogle Scholar
Stauffer, D. 1979, PhR, 54, 110.1016/0370-1573(79)90060-7CrossRefGoogle Scholar
Tempel, E., et al. 2014, A&A, 566, A1 10.1051/0004-6361/201423585CrossRefGoogle Scholar
Theodoridis, S., & Koutroumbas, K. 2009, in Pattern Recognition, ed. Sergios, T., & Konstantinos, K. (4th edn.; Boston: Academic Press), 595Google Scholar
Tully, R. B. 2015, AJ, 149, 5410.1088/0004-6256/149/2/54CrossRefGoogle Scholar
Tully, R. B., Courtois, H., Hoffman, Y., & Pomarède, D. 2014, Nature, 513, 71 10.1038/nature13674CrossRefGoogle Scholar
Wen, Z. L., Han, J. L., & Liu, F. S. 2012, ApJS, 199, 3410.1088/0067-0049/199/2/34CrossRefGoogle Scholar
Zeldovich, Y. B., Einasto, J., & Shandarin, S. F. 1982, Nature, 300, 407 10.1038/300407a0CrossRefGoogle Scholar
Zúñiga, J. M., Caretta, C. A., González, A. P., & Garca-Manzanárez, E. 2024, RMxAA, 60, 14110.22201/ia.01851101p.2024.60.01.11CrossRefGoogle Scholar