  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 $z=2.2$,
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 spherical
maps. The catalogue provides the position and uncertainty of each detection for
each redshift slice. We assess the quality of the detections with several
metrics, which show improvement with respect to previous public catalogues
obtained with similar methods. We also detect a highly significant correlation
between our filament catalogue and galaxy cluster catalogues built from
microwave observations of the Planck Satellite and the Atacama Cosmology
Telescope.
