The XXL Survey
Bibliographic record
Abstract
Context.X-ray observations of galaxy clusters are impacted by the presence of active galactic nuclei (AGNs) in a manner that is challenging to quantify, leading to biases in the detection and measurement of cluster properties for both astrophysics and cosmological applications. Aims.We detect and characterise clusters contaminated by central AGNs within the XXL survey footprint and provide a systematic assessment of the cosmological impact of such systems in X-ray cluster samples. Methods.We introduce a new automated class for AGN-contaminated (AC) clusters in the XXL source detection pipeline. The majority of these systems are otherwise missed by current X-ray cluster-detection methods. The AC selection is also effective in distinguishing AGN and cool-core presence using supplementary optical and infrared information. Results.We present 33 AC objects, including 25 clusters in the redshift range, 0.14 ≤ z ≤ 1.03, and eight other sources with significantly peaked central profiles based on X-ray observations. Six of these are new confirmed clusters. We computed the missed fraction of the XXL survey, which is defined as the fraction of genuine clusters that are undetected due to their centrally peaked X-ray profiles. We report seven undetected AC clusters abovez > 0.6, in the range where X-ray cluster detection efficiency drops significantly. The missed fraction is estimated to be at the level of 5% for the 50 square-degree XXL area. The impact on cosmological estimates from missed clusters is negligible for XXL, but it produces a tension of ∼3σwith the fiducial cosmology when considering larger survey areas. Conclusions.This work demonstrates the first systematic attempt to quantify the percentage of missed clusters in X-ray surveys as a result of central AGN contamination. Looking towards surveys such as eROSITA and Athena, larger areas and increased sensitivity will significantly enhance cluster detection, and therefore robust methods for characterising AGN contamination will be crucial for precise cluster cosmology, particularly in the redshiftz > 1 regime.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.041 | 0.029 |
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".