A Fast and Simple Algorithm for Detecting Large Scale Structures

TL;DR

The paper introduces a gravitational potential method (GPM) for efficiently detecting large-scale structures like superclusters in galaxy distributions by analyzing local gravitational potential fluctuations.

Contribution

It presents a novel, fast algorithm based on gravitational potential analysis to identify superclusters, demonstrating its effectiveness on galaxy cluster data.

Findings

Detected a massive supercluster with 35 galaxy clusters within 51 Mpc radius.

Confirmed the method's results align with other established detection techniques.

Showed GPM is a simple, fast, and powerful tool for large dataset analysis.

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. 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 cluster finder useful for analyzing large datasets.