A Fast and Simple Algorithm for Detecting Large Scale Structures

Giovanni C. Baiesi Pillastrini *

Sezione di Ricerca Spettroscopia - U.A.I. C/O I.A.S.F. via del Fosso del Cavaliere 100, 00133 Roma, Italy

*Author to whom correspondence should be addressed.


Abstract

Aims: We propose a gravitational potential method (GPM) as a supercluster finder based on the analysis of the local gravitational potential distribution measured by fast and simple algorithm applied to a spatial distribution of mass tracers. 

Methodology: the GPM performs a two-step exploratory data analysis: first, it measures the comoving local gravitational potential generated by neighboring mass tracers at the position of a test point-like mass tracer. The computation extended to all mass tracers of the sample provides a detailed map of the negative potential fluctuations. The most negative gravitational potential is provided by the highest mass density or, in other words, the deeper is a potential fluctuations in a certain region of space and denser are the mass tracers in that region. Therefore, from a smoothed potential distribution, the deepest potential well detects unambiguously a high concentration in the mass tracer distribution. Second, applying a density contrast criterion to that mass concentration, a central bound core may be identify and quantify in terms of memberships and total mass. 

Results: using a complete volume-limited sample of galaxy clusters, a huge concentration of galaxy clusters has been detected. In its central region, 35 clusters seem to form a massive and bound core  enclosed in a spherical volume of  51 Mpc radius, centered at  Galactic coordinates l ~ 63°.7, b ~ 63°.7 and at  redshift ~ .36. It has a velocity dispersion of 1,183 Km/s with an estimated virial mass of 2.67± .80 x 1016 Mʘ.   

Conclusions: the substantial agreement of our findings compared with those obtained by different methodologies, confirms the GPM as a straightforward and powerful as well as fast supercluster finder useful for analyzing large datasets. 

Keywords: Methods, data analysis - galaxies, clusters - large scale structures of the universe


How to Cite

C. Baiesi Pillastrini, G. (2013). A Fast and Simple Algorithm for Detecting Large Scale Structures. Physical Science International Journal, 3(4), 452–471. Retrieved from https://journalpsij.com/index.php/PSIJ/article/view/159

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