Hostname: page-component-5f56664f6-ldbxw Total loading time: 0 Render date: 2025-05-07T16:09:41.589Z 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: [email protected]
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
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, 2Google Scholar
Abell, G. O. 1958, ApJS, 3, 211Google Scholar
Abell, G. O. 1961, AJ, 66, 607Google Scholar
Abell, G. O., Corwin, H. Jr. G., & Olowin, R. P. 1989, ApJS, 70, 1Google 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, 97Google Scholar
Bahcall, N. A. 1996, Preprint (arXiv:astro-ph/9611148v1).Google Scholar
Bahcall, N. A., & Soneira, R. M. 1984, ApJ, 277, 27Google Scholar
Bardelli, S., et al. 1994, MNRAS, 267, 665Google Scholar
Bardelli, S., Zucca, E., Zamorani, G., Moscardini, L., & Scaramella, R. 2000, MNRAS, 312, 540Google Scholar
Beers, T. C., Flynn, K., & Gebhardt, K. 1990, AJ, 100, 32Google Scholar
Beers, T. C., Geller, M. J., & Huchra, J. P. 1982, ApJ, 257, 23Google 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, 23Google Scholar
Böhringer, H., & Chon, G. 2021, A&A, 656, A144 Google Scholar
Böhringer, H., Chon, G., & Trümper, J. 2021, A&A, 651, A16 Google Scholar
Bolton, A. S., et al. 2012, AJ, 144, 144Google Scholar
Breen, J., Raychaudhury, S., Forman, W., & Jones, C. 1994, ApJ, 424, 59Google Scholar
Bryan, G. L., & Norman, M. L. 1998, ApJ, 495, 80Google Scholar
Caretta, C. A., Andernach, H., Chow-Martínez, M., Coziol, R., De Anda-Suárez, J., Hernández-Aguayo C., & et al. 2023, RMxAA, 59, 345 Google Scholar
Caretta, C. A., Maia, M. A., Kawasaki, W., & Willmer, C. N. A. 2002, AJ, 123, 1200Google Scholar
Chiueh, T., & He, X.-G. 2002, PhRvD, 65, 123518Google Scholar
Chon, G., Böhringer, H., & Nowak, N. 2013, MNRAS, 429, 3272Google Scholar
Chon, G., Böhringer, H., & Zaroubi, S. 2015, A&A, 575, L14 Google 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, 4073Google 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, 505Google Scholar
Colless, M., et al. (the 2dFGRS team) 2001, MNRAS, 328, 1039Google Scholar
Costa-Duarte, M. V., Sodré, L. Jr., & Durret, F. 2011, MNRAS, 411, 1716Google Scholar
Davis, M., Efstathiou, G., Frenk, C. S., & White, S. D. M. 1985, ApJ, 292, 371Google Scholar
Dünner, R., Araya, P. A., Meza, A., & Reisenegger, A. 2006, MNRAS, 366, 803Google Scholar
Edelsbrunner, H., & Mücke, E. P. 1994, ACM TG, 13, 43Google Scholar
Einasto, J. 2010, AIP Conference Proceedings, Vol. 1205, 72Google Scholar
Einasto, J., et al. 2007, A&A, 462, 811Google Scholar
Einasto, J., Klypin, A. A., Saar, E., & Shandarin, S. F. 1984, MNRAS, 206, 529Google Scholar
Einasto, J., Suhhonenko, I., Liivamägi, L. J., & Einasto, M. 2018, A&A, 616, A141 Google Scholar
Einasto, J., Suhhonenko, I., Liivamägi, L. J., & Einasto, M. 2019, A&A, 623, A97 Google Scholar
Einasto, M., Einasto, J., Tago, E., Müller, V., & Andernach, H. 2001, AJ, 122, 2222Google Scholar
Einasto, M., et al. 2007, A&A, 464, 815Google Scholar
Einasto, M., et al. 2024, A&A, 681, A91 Google Scholar
Einasto, M., et al. 2015, A&A, 580, A69 Google Scholar
Einasto, M., et al. 2021, A&A, 649, A51 Google Scholar
Einasto, M., et al. 2016, A&A, 595, A70 Google Scholar
Einasto, M., et al. 2007, A&A, 476, 697Google Scholar
Einasto, M., et al. 2008, ApJ, 685, 83Google 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, 2064Google Scholar
Gramann, M., & Suhhonenko, I. 2002, MNRAS, 337, 1417Google Scholar
Hambly, N. C., et al. 2001, MNRAS, 326, 1279Google Scholar
Heinämäki, P., Teerikorpi, P., Douspis, M., Nurmi, P., Einasto, M. ang Gramann, M., Nevalainen, J., & Saar, E. 2022, A&A, 668, A37Google Scholar
Hogg, D. W. 2000, Preprint (arXiv:astro-ph/9905116v4)Google Scholar
Huchra, J. P., & Geller, M. J. 1982, ApJ, 257, 423Google Scholar
Jones, D. H., et al. 2009, MNRAS, 399, 683Google Scholar
Koester, B. P., et al. 2007, ApJ, 660, 239Google 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 Google Scholar
Libeskind, N. I., et al. 2018, MNRAS, 473, 1195 Google Scholar
Liivamägi, L. J., Tempel, E., & Saar, E. 2012, A&A, 539, A80 Google Scholar
Liu, T., et al. 2015, ApJS, 216, 28Google 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, 1Google Scholar
Luparello, H., Lares, M., Lambas, D. G., & Padilla, N. 2011, MNRAS, 415, 964Google Scholar
Marini, F., et al. 2004, MNRAS, 353, 1219Google 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, 255Google Scholar
Melott, A. L., et al. 1983, PhRvL, 51, 935Google Scholar
Miller, C. J., et al. 2005, AJ, 130, 968Google Scholar
Nakamura, O., et al. 2003, AJ, 125, 1682Google Scholar
Ochsenbein, F., Bauer, P., & Marcout, J. 2000, A&AS, 143, 23Google Scholar
Oort, J. H. 1983, A&A, 21, 373Google Scholar
Pearson, D. W., Batiste, M., & Batuski, D. 2014, MNRAS, 441, 1601Google 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 Google 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 Google 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, 2976Google 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, 62Google Scholar
Santiago-Bautista, I., Caretta, C. A., Bravo-Alfaro, H., Pointecouteau, E., & Andernach, H. 2020, A&A, 637, A31 Google Scholar
Sargent, W. L. W., & Turner, E. L. 1977, ApJ, 212, L3 Google Scholar
Schneider, P. 2015, Extragalactic Astronomy and Cosmology: An Introduction. Second (Springer)Google Scholar
Serna, A., & Gerbal, D. 1996, A&A, 309, 65Google Scholar
Shandarin, S. F. 1983, SvAL, 9, 104Google Scholar
Shandarin, S. F., Sheth, J. V., & Sahni, V. 2004, MNRAS, 353, 162Google Scholar
Sheth, R., & Diaferio, A. 2011, MNRAS, 417, 4Google Scholar
Small, T. A., Ma, C. P., Sargent, W. L. W., & Hamilton, D. 1998, ApJ, 492, 45 Google Scholar
Small, T. A., Sargent, W. L. W., & Hamilton, D. 1997, ApJS, 111, 1Google Scholar
Smith, A. G., Hopkins, A. M., Hunstead, R. W., & Pimbblet, K. A. 2012, MNRAS, 422, 25Google Scholar
Stauffer, D. 1979, PhR, 54, 1Google Scholar
Tempel, E., et al. 2014, A&A, 566, A1 Google 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, 54Google Scholar
Tully, R. B., Courtois, H., Hoffman, Y., & Pomarède, D. 2014, Nature, 513, 71 Google Scholar
Wen, Z. L., Han, J. L., & Liu, F. S. 2012, ApJS, 199, 34Google Scholar
Zeldovich, Y. B., Einasto, J., & Shandarin, S. F. 1982, Nature, 300, 407 Google Scholar
Zúñiga, J. M., Caretta, C. A., González, A. P., & Garca-Manzanárez, E. 2024, RMxAA, 60, 141Google Scholar