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Cosmic voids and their corresponding redshift-projected mass densities, knownas troughs, play an important role in our attempt to model the large-scalestructure of the Universe. Understanding these structures enables us to comparethe standard model with alternative cosmologies, constrain the dark energyequation of state, and distinguish between different gravitational theories. Inthis paper, we extend the subspace-constrained mean shift algorithm, a recentlyintroduced method to estimate density ridges, and apply it to 2D weak lensingmass density maps from the Dark Energy Survey Y1 data release to identifycurvilinear filamentary structures. We compare the obtained ridges withprevious approaches to extract trough structure in the same data, and applycurvelets as an alternative wavelet-based method to constrain densities. Wethen invoke the Wasserstein distance between noisy and noiseless simulations tovalidate the denoising capabilities of our method. Our results demonstrate theviability of ridge estimation as a precursor for denoising weak lensingobservables to recover the large-scale structure, paving the way for a moreversatile and effective search for troughs.

Ridges in the Dark Energy Survey for cosmic trough identification