Multiscale probability mapping: groups, clusters and an algorithmic search for filaments in SDSS
Anthony G. Smith, Andrew M. Hopkins, Richard W. Hunstead, Kevin A., Pimbblet

TL;DR
This paper introduces a multiscale probability mapping algorithm to detect galaxy overdensities and filaments in SDSS data, providing a new method for structure identification across various scales.
Contribution
The study develops a novel multiscale structure detection algorithm combining density and shape statistics, and applies it to create a galaxy group, cluster, and filament catalog from SDSS data.
Findings
Identified 53 filaments as elongated unions of groups and clusters.
Most structures have velocity dispersions between 50 and 400 km/s.
Confirmed a continuous increase in velocity dispersion with radius.
Abstract
We have developed a multiscale structure identification algorithm for the detection of overdensities in galaxy data that identifies structures having radii within a user-defined range. Our "multiscale probability mapping" technique combines density estimation with a shape statistic to identify local peaks in the density field. This technique takes advantage of a user-defined range of scale sizes, which are used in constructing a coarse-grained map of the underlying fine-grained galaxy distribution, from which overdense structures are then identified. In this study we have compiled a catalogue of groups and clusters at 0.025 < z < 0.24 based on the Sloan Digital Sky Survey, Data Release 7, quantifying their significance and comparing with other catalogues. Most measured velocity dispersions for these structures lie between 50 and 400 km/s. A clear trend of increasing velocity dispersion…
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