Understanding X-ray and optical selection of galaxy clusters: a comparison of the XXL and CAMIRA cluster catalogues obtained in the common XXL-HSC SSP area
Bibliographic record
Abstract
ABSTRACT Large samples of galaxy clusters provide knowledge of both astrophysics in the most massive virialized environments and the properties of the cosmological model that defines our Universe. However, an important issue that affects the interpretation of galaxy cluster samples is the role played by the selection waveband and the potential for this to introduce a bias in the physical properties of clusters thus selected. We aim to investigate waveband-dependent selection effects in the identification of galaxy clusters by comparing the X-ray MultiMirror (XMM) Ultimate Extra-galactic Survey (XXL) and Subaru Hyper Suprime-Cam (HSC) CAMIRA cluster samples identified from a common 22.6 deg2 sky area. We compare 150 XXL and 270 CAMIRA clusters in a common parameter space defined by X-ray aperture brightness and optical richness. We find that 71/150 XXL clusters are matched to the location of a CAMIRA cluster, the majority of which (67/71) display richness values N > 15 that exceed the CAMIRA catalogue richness threshold. We find that 67/270 CAMIRA clusters are matched to the location of an XXL cluster (defined within XXL as an extended X-ray source). Of the unmatched CAMIRA clusters, the majority display low X-ray fluxes consistent with the lack of an XXL counterpart. However, a significant fraction (64/107) CAMIRA clusters that display high X-ray fluxes are not associated with an extended source in the XXL catalogue. We demonstrate that this disparity arises from a variety of effects including the morphological criteria employed to identify X-ray clusters and the properties of the XMM PSF.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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) | 0.002 | 0.000 |
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".