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Record W4360825581 · doi:10.1051/0004-6361/202244898

The XXL Survey

2023· article· en· W4360825581 on OpenAlexaff
S Bhargava, C. Garrel, E. Koulouridis, M. Pierre, I. Valtchanov, Nicolas Cerardi, B. J. Maughan, M. Aguena, Christophe Benoist, Cale Baguley, M. E. Ramos-Ceja, C. Adami, L. Chiappetti, C. Vignali, J. P. Willis

Bibliographic record

VenueAstronomy and Astrophysics · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGalaxies: Formation, Evolution, Phenomena
Canadian institutionsUniversity of Victoria
FundersScience and Technology Facilities Council
KeywordsAstrophysicsPhysicsGalaxy clusterCluster (spacecraft)RedshiftActive galactic nucleusGalaxyCosmologyAstronomy

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.041
Threshold uncertainty score0.136

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0410.029

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.

Opus teacher head0.010
GPT teacher head0.207
Teacher spread0.197 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2023
Admission routes1
Has abstractyes

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