A Fast and Simple Algorithm for Detecting Large Scale Structures
Giovanni C. Baiesi Pillastrini

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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsGalaxies: Formation, Evolution, Phenomena · Scientific Research and Discoveries · Gamma-ray bursts and supernovae
