Discriminating Topology in Galaxy Distributions using Network Analysis
© 2016 The Authors Published by Oxford University Press on behalf of the Royal Astronomical Society. The large-scale distribution of galaxies is generally analysed using the two-point correlation function. However, this statistic does not capture the topology of the distribution, and it is necessary...
| Main Authors: | , , , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
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Oxford University Press (OUP)
2021
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| Online Access: | https://hdl.handle.net/1721.1/134452 |
| _version_ | 1811068933385486336 |
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| author | Hong, Sungryong Coutinho, Bruno C Dey, Arjun Barabási, Albert-L Vogelsberger, Mark Hernquist, Lars Gebhardt, Karl |
| author2 | Massachusetts Institute of Technology. Department of Physics |
| author_facet | Massachusetts Institute of Technology. Department of Physics Hong, Sungryong Coutinho, Bruno C Dey, Arjun Barabási, Albert-L Vogelsberger, Mark Hernquist, Lars Gebhardt, Karl |
| author_sort | Hong, Sungryong |
| collection | MIT |
| description | © 2016 The Authors Published by Oxford University Press on behalf of the Royal Astronomical Society. The large-scale distribution of galaxies is generally analysed using the two-point correlation function. However, this statistic does not capture the topology of the distribution, and it is necessary to resort to higher order correlations to break degeneracies. We demonstrate that an alternate approach using network analysis can discriminate between topologically different distributions that have similar two-point correlations. We investigate two galaxy point distributions, one produced by a cosmological simulation and the other by a Lévy walk. For the cosmological simulation, we adopt the redshift z = 0.58 slice from Illustris and select galaxies with stellar masses greater than 108 M⊙. The two-point correlation function of these simulated galaxies follows a single power law, ζ(r) ~ r-1.5. Then, we generate Lévy walks matching the correlation function and abundance with the simulated galaxies. We find that, while the two simulated galaxy point distributions have the same abundance and two-point correlation function, their spatial distributions are very different; most prominently, filamentary structures, absent in Lévy fractals. To quantify these missing topologies, we adopt network analysis tools and measure diameter, giant component, and transitivity from networks built by a conventional friends-of-friends recipe with various linking lengths. Unlike the abundance and two-point correlation function, these network quantities reveal a clear separation between the two simulated distributions; therefore, the galaxy distribution simulated by Illustris is not a Lévy fractal quantitatively. We find that the described network quantities offer an efficient tool for discriminating topologies and for comparing observed and theoretical distributions. |
| first_indexed | 2024-09-23T08:03:05Z |
| format | Article |
| id | mit-1721.1/134452 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T08:03:05Z |
| publishDate | 2021 |
| publisher | Oxford University Press (OUP) |
| record_format | dspace |
| spelling | mit-1721.1/1344522023-02-22T21:24:55Z Discriminating Topology in Galaxy Distributions using Network Analysis Hong, Sungryong Coutinho, Bruno C Dey, Arjun Barabási, Albert-L Vogelsberger, Mark Hernquist, Lars Gebhardt, Karl Massachusetts Institute of Technology. Department of Physics MIT Kavli Institute for Astrophysics and Space Research © 2016 The Authors Published by Oxford University Press on behalf of the Royal Astronomical Society. The large-scale distribution of galaxies is generally analysed using the two-point correlation function. However, this statistic does not capture the topology of the distribution, and it is necessary to resort to higher order correlations to break degeneracies. We demonstrate that an alternate approach using network analysis can discriminate between topologically different distributions that have similar two-point correlations. We investigate two galaxy point distributions, one produced by a cosmological simulation and the other by a Lévy walk. For the cosmological simulation, we adopt the redshift z = 0.58 slice from Illustris and select galaxies with stellar masses greater than 108 M⊙. The two-point correlation function of these simulated galaxies follows a single power law, ζ(r) ~ r-1.5. Then, we generate Lévy walks matching the correlation function and abundance with the simulated galaxies. We find that, while the two simulated galaxy point distributions have the same abundance and two-point correlation function, their spatial distributions are very different; most prominently, filamentary structures, absent in Lévy fractals. To quantify these missing topologies, we adopt network analysis tools and measure diameter, giant component, and transitivity from networks built by a conventional friends-of-friends recipe with various linking lengths. Unlike the abundance and two-point correlation function, these network quantities reveal a clear separation between the two simulated distributions; therefore, the galaxy distribution simulated by Illustris is not a Lévy fractal quantitatively. We find that the described network quantities offer an efficient tool for discriminating topologies and for comparing observed and theoretical distributions. 2021-10-27T20:05:04Z 2021-10-27T20:05:04Z 2016 2019-06-18T11:15:52Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/134452 Hong, S., et al. "Discriminating Topology in Galaxy Distributions Using Network Analysis." Monthly Notices of the Royal Astronomical Society 459 3 (2016): 2690-700. en 10.1093/MNRAS/STW803 Monthly Notices of the Royal Astronomical Society Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Oxford University Press (OUP) arXiv |
| spellingShingle | Hong, Sungryong Coutinho, Bruno C Dey, Arjun Barabási, Albert-L Vogelsberger, Mark Hernquist, Lars Gebhardt, Karl Discriminating Topology in Galaxy Distributions using Network Analysis |
| title | Discriminating Topology in Galaxy Distributions using Network Analysis |
| title_full | Discriminating Topology in Galaxy Distributions using Network Analysis |
| title_fullStr | Discriminating Topology in Galaxy Distributions using Network Analysis |
| title_full_unstemmed | Discriminating Topology in Galaxy Distributions using Network Analysis |
| title_short | Discriminating Topology in Galaxy Distributions using Network Analysis |
| title_sort | discriminating topology in galaxy distributions using network analysis |
| url | https://hdl.handle.net/1721.1/134452 |
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