A tight relation between the distribution of globular clusters and dark matter in AS1063

TL;DR

This study leverages deep JWST GLIMPSE data of the cluster AS1063 to detect tens of thousands of globular clusters (GCs) and compare their spatial distribution to a JWST-derived lensing mass map. A smoothing kernel is used to transform the discrete GC positions into a continuous GC density $\rho_{GC}$, which closely tracks the lensing convergence $\kappa$ over the region constrained by lensing, and scales as $<\kappa> = 0.4\, n_{GC}^{0.7}$. The results demonstrate that GC distributions can serve as tracers of the dark matter distribution, offering a practical mass proxy in clusters where strong lensing constraints are sparse or absent, and revealing a GC profile that broadly follows a $r^{-2}$ form out to near the virial radius. This work highlights a tangible method to extend mass mapping in galaxy clusters using GC catalogs, potentially enhancing mass models in low-redshift, subcritical systems and informing DM studies across cosmic time.

Abstract

Based on deep high resolution JWST images of AS1063, and after a careful masking of artifacts, extended features in the cluster, and background galaxies (including known lensed ones), we have identified tens of thousands of unresolved point sources in the central region of the galaxy cluster. We extended the identification of these point sources up to 1.18 Mpc from the center of the cluster using data in the second module. Most of these sources are expected to be globular clusters orbiting in the deep potential well of the cluster, but also the surviving compact cores of satellite galaxies. We study the distribution of the globular clusters and compared it with the distribution of mass from a lens model derived from the same JWST data. We find a very tight correlation between the two distributions, but also some differences, including a more concentrated distribution for the globular clusters than for dark matter. We explored the possibility of using the distribution of globular clusters as a proxy for the lensing mass. We find that a simple smoothing kernel can transform the discrete distribution of point sources into a continuous two-dimensional distribution that matches well the lensing convergence. This suggests that globular clusters can be used as tracers of the dark matter distribution in other massive clusters where gravitational lensing constraints are scarce but globular clusters can be detected more easily, for instance in low redshift galaxy clusters.

Figures (7)

• Figure 1: Color image of AS1063 combining 12 HST and JWST filters (range 0.4--5 micron). Three critical curves from our lens model are shown at $z=0.73$ (cyan), $z=1.23$ (yellow), and $z=3$ (red). The yellow arrows mark two families of lensed galaxies at $z\approx1.23$ intersected by the critical curve at that redshift, while the cyan arrows mark one family of lensed images from a galaxy at $z=0.73$ intersected by the cyan critical curve. White circles mark the position of prominent elliptical galaxies. The small panels A, B, D and E show zoomed-in regions of a filtered version of the sum of JWST's SW filters. Yellow circles mark the position of automatically detected point sources in the filtered images. The white contours show the masked regions where no point source detection is performed except for the very central region (panel D) where identification of point sources is done visually inside the 1" radius masked region. The mask of the full region containing bright and/or extended sources as well as artifacts is shown in panel C (bottom right).
• Figure 2: Map of GC distribution obtained from the data (red points, 28026) and inpainted (black points, 3501) in the masked regions. Some artifacts (diffraction spikes) and large lensed galaxies (giant arcs) are obvious in the distribution of inpainted points. Big blue circles mark the position of prominent members (white circles in Fig. 1).
• Figure 3: Number density of GCs vs mean convergence computed in boxes of $3"\times3"$. The dashed line corresponds to $y=0.4x^{0.7}$ and fits reasonably well the observed correlation.
• Figure 4: Map of GC density after inpainting the masked regions (gray). The green contours are for the GC density map (gray scale image) while the cyan contours are for the lens model convergence map from paper-I. The two thicker cyan contours correspond to $\kappa=0.3$ and $\kappa=1.8$.
• Figure 5: Scatter plot of convergence vs GC density maps. The solid line is the $x=y$ model. The GC density, $\rho_{\rm gc}$ has been re-scaled by the factor $\beta$ that minimizes the variance of the difference map $r=\kappa-\beta\times\rho_{\rm gc}$.