Key points are not available for this paper at this time.
The assembly of galaxy groups and clusters occur through dynamical interactions of smaller systems, resulting in the formation of a diffuse stellar halo known as the intragroup or intracluster light (IGL or ICL). By preserving the records of these interactions, the IGL and ICL provide valuable insight into the growth history of galaxy groups and clusters. Groups are especially interesting because they represent the link between galactic halos and massive clusters. However, the low surface brightness of this diffuse light makes it extremely challenging to detect individually. Recent deep wide-field imaging surveys allow us to push such measurements to lower brightness limits by stacking data for large ensembles of groups, thereby suppressing the noise and biases in the measurements. In this work, we present a special-purpose pipeline to reprocess individual r -band Kilo-Degree Survey (KiDS) exposures to optimise the IGL detection. Using an initial sample of 2385 groups with at least five spectroscopically confirmed member galaxies from the Galaxy and Mass Assembly (GAMA) survey and deep images from KiDS (reprocessed with our updated pipeline), we present the first robust measurement of IGL from a large group sample (∼750) down to 31−32 mag/arcsec 2 (varying in different stacked bins). We also compare our stacked IGL measurements to predictions from matched mock observations from the Hydrangea cosmological hydrodynamic simulations. Systematics in the imaging data can affect IGL measurements, even with our special-purpose pipeline. However, with a large sample and optimised analysis, we can place well-constrained upper and lower limits on the IGL fraction (3−21%) for our group ensemble across 0.09 ≤ z ≤ 0.27 and 12.5 ≤ log 10 M 200 /M ⊙ ≤ 14.0. This work explores the potential performance of stacked statistical analysis of diffuse light in large samples of systems from next-generation observational programs such as Euclid and the Vera C. Rubin Observatory’s Legacy Survey of Space and Time (LSST).
Ahad et al. (Mon,) studied this question.