A novel Cosmic Filament catalogue from SDSS data
Javier Carr\'on Duque, Marina Migliaccio, Domenico Marinucci, Nicola, Vittorio

TL;DR
This paper introduces a new, deep catalogue of Cosmic Filaments from SDSS data, utilizing advanced algorithms and machine learning, covering redshifts up to 2.2, and demonstrating improved detection quality and correlation with galaxy clusters.
Contribution
The paper presents a novel filament detection method combining Subspace-Constrained Mean-Shift and machine learning, producing one of the deepest filament catalogues from SDSS data.
Findings
Improved filament detection metrics over previous catalogues.
Filament catalogue shows significant correlation with galaxy cluster data.
Reconstruction extends up to redshift z=2.2, covering a large cosmic volume.
Abstract
In this work we present a new catalogue of Cosmic Filaments obtained from the latest Sloan Digital Sky Survey (SDSS) public data. In order to detect filaments, we implement a version of the Subspace-Constrained Mean-Shift algorithm, boosted by Machine Learning techniques. This allows us to detect cosmic filaments as one-dimensional maxima in the galaxy density distribution. Our filament catalogue uses the cosmological sample of SDSS, including Data Release 16, so it inherits its sky footprint (aside from small border effects) and redshift coverage. In particular, this means that, taking advantage of the quasar sample, our filament reconstruction covers redshifts up to , making it one of the deepest filament reconstructions to our knowledge. We follow a tomographic approach and slice the galaxy data in 269 shells at different redshift. The reconstruction algorithm is applied to 2D…
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