MACS0416 lens model and surface mass density map from GLASS spectroscopy

MACS0416lensfig

In Hoag et al. (2016), the GLASS team used the GLASS spectroscopy combined with the deep imaging from the Hubble Frontier Fields (HFF) to produce a gravitational lens model of the galaxy cluster MACSJ0416.1-2403 (MACS0416), following the approach by Wang et al. (2015) who modeled Abell 2744. The GLASS data allowed the team to measure the spectroscopic redshifts of galaxies that are multiply imaged by the cluster, significantly improving the constraints on the lens model. Figures showing the spectroscopic confirmations of multiply-imaged galaxies with the GLASS data are available on this page. The above figure on the left shows a co-added F105W image from the HFF, Cluster Lensing and Supernova survey with Hubble (CLASH), and the GLASS direct imaging. Overlaid on the figure is the critical curve from the lens model (dark green line) with the multiple images (multi-colored circles) that have been discovered in the cluster field. The Gold circles enclose spectroscopically confirmed multiple images from the GLASS spectroscopy and previous spectroscopic programs. All of the other colored circles lack spectroscopy, so they were vetted more carefully. We only use the Gold and Silver objects to constrain the lens model as they are the most trustworthy multiple image candidates.

From the lens model, the team derived a map of the total surface mass density throughout the cluster. Using deep mid-infrared data from the Spitzer Frontier Fields program, the team obtained a map of the stellar surface mass density in the cluster. The figure on the right shows the projected stellar to total mass ratio (f*) throughout the central ~500 kpc of the cluster. The f* varies significantly with distance from the two BCGs (black dots). The mean projected stellar to total mass ratio is <f*> = 0.009 +/- 0.003 (stat.), using a diet-Salpeter IMF, in agreement with other measurements of <f*> in massive cluster environments.

For more information seeĀ Hoag et al. (2016).