Discriminating topology in galaxy distributions using network analysis
Coutinho, Bruno C.
Barabási, Albert -L.
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CitationHong, Sungryong, Bruno C. Coutinho, Arjun Dey, Albert -L. Barabási, Mark Vogelsberger, Lars Hernquist, and Karl Gebhardt. 2016. “Discriminating Topology in Galaxy Distributions Using Network Analysis.” Monthly Notices of the Royal Astronomical Society 459 (3): 2690–2700. https://doi.org/10.1093/mnras/stw803.
AbstractThe 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 Levy walk. For the cosmological simulation, we adopt the redshift z = 0.58 slice from Illustris and select galaxies with stellar masses greater than 10(8) M-circle dot. The two-point correlation function of these simulated galaxies follows a single power law, xi(r) similar to r(-1.5). Then, we generate Levy 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 Levy 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 Levy fractal quantitatively. We find that the described network quantities offer an efficient tool for discriminating topologies and for comparing observed and theoretical distributions.
Citable link to this pagehttp://nrs.harvard.edu/urn-3:HUL.InstRepos:41381855
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