Ridges in the Dark Energy Survey for cosmic trough identification
  Cosmic voids and their corresponding redshift-aggregated projections of mass
densities, known as troughs, play an important role in our attempt to model the
large-scale structure of the Universe. Understanding these structures leads to
tests comparing the standard model with alternative cosmologies, constraints on
the dark energy equation of state, and provides evidence to differentiate among
gravitational theories. In this paper, we extend the subspace-constrained mean
shift algorithm, a recently introduced method to estimate density ridges, and
apply it to 2D weak-lensing mass density maps from the Dark Energy Survey Y1
data release to identify curvilinear filamentary structures. We compare the
obtained ridges with previous approaches to extract trough structure in the
same data, and apply curvelets as an alternative wavelet-based method to
constrain densities. We then invoke the Wasserstein distance between noisy and
noiseless simulations to validate the denoising capabilities of our method. Our
results demonstrate the viability of ridge estimation as a precursor for
denoising weak lensing quantities to recover the large-scale structure, paving
the way for a more versatile and effective search for troughs.
