  Cosmic voids and their corresponding redshift-projected mass densities, known
as troughs, play an important role in our attempt to model the large-scale
structure of the Universe. Understanding these structures enables us to compare
the standard model with alternative cosmologies, constrain the dark energy
equation of state, and distinguish between different 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
observables to recover the large-scale structure, paving the way for a more
versatile and effective search for troughs.
