Searching for Dark Structures: A Comparison of Weak Lensing Convergence Maps and Lensing-Weighted Galaxy Density Maps

TL;DR

This study tests the link between dark matter and galaxy distributions by comparing DES Year 3 weak-lensing convergence maps with lensing-weighted galaxy density maps constructed from the DES Y3 Gold catalog. The authors build $\kappa_g$ using the lensing weight $q(r, p_s)$ and the 2D galaxy density fluctuations, then scale the galaxy convergence to align with the WL convergence derived from shear data reconstructed via Wiener and GLIMPSE priors across four tomographic bins. By analyzing residuals between $\kappa$ and the scaled $\kappa_g$, they identify dark-structure candidates, finding 22 peaks with 7 deemed most probable after quality checks and depth considerations. The methodology demonstrates a promising approach for dark matter mapping in current and upcoming surveys (e.g., \\textit{Euclid}, LSST, and Roman), enabling systematic searches for dark structures and tests of galaxy bias on intermediate scales.

Abstract

We present the result of a comparison between the dark matter distribution inferred from weak gravitational lensing and the observed galaxy distribution to identify dark structures with a high dark matter-to-galaxy density ratio. To do this, we use weak lensing convergence maps from the Dark Energy Survey Year 3 data, and construct corresponding galaxy convergence maps at $z\lesssim1.0$, representing projected galaxy number density fluctuations weighted by lensing efficiency. The two maps show overall agreement. However, we could identify 22 regions where the dark matter density exhibits an excess compared to the galaxy density. After carefully examining the survey depths and proximity to survey boundaries, we select seven of the most probable candidates for dark structures. This sample provides valuable testbeds for further investigations into dark matter mapping. Moreover, our method will be very useful for future studies of dark structures as large-scale weak-lensing surveys become available, such as the $\textit{Euclid}$ mission, the Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST), and the Nancy Grace Roman Space Telescope.

Figures (7)

• Figure 1: The Wiener weak lensing convergence maps from DESWLmap before smoothing is shown for each tomographic bin. Gray boundaries show the DES Y3 survey footprint.
• Figure 2: For each tomographic bin, the black histograms are the source redshift distributions $p_s(z)$ from SOMPZ and red solid lines are the lensing efficiency curve $q(z_l, p_s)$. The histograms are normalized so that the total area under each curve is equal to 1. The red vertical dashed lines represent the peak of the lensing efficiency curve. The red shaded areas indicate the interval of lens redshifts where the lensing efficiency is larger than 75% of its peak value.
• Figure 3: The source redshift distributions $p_s(z)$ for each tomographic bin. Red and blue solid lines show the specific source redshift distribution for 226-th and 3101-th HEALPix pixel. The background gray histograms are the fiducial source redshift distributions as presented previously in Figure \ref{['fig:lensweight_fig2']}. The gray histograms are differ from solid lines as it shows the marginalized source redshift distribution regardless of the sky position.
• Figure 4: The galaxy convergence maps, $\kappa_g$, for each tomographic bin are shown. Each map is smoothed with a 10 arcmin FWHM Gaussian kernel.
• Figure 5: The histograms of the Wiener weak lensing convergence in gray, the galaxy convergence in red lines, and the scaled galaxy convergence in blue lines are shown for each tomographic bin. All histograms are normalized such that the area under each curve is equal to 1. The black and blue dashed lines are the fitted Gaussian distributions for the weak lensing convergence and the scaled galaxy convergence, respectively.