The intragroup light in KiDS+GAMA groups
Bibliographic record
Abstract
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).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".