X-ray versus infrared selection of distant galaxy clusters: a case study using the XMM–LSS and SpARCS cluster samples
Bibliographic record
Abstract
We present a comparison of two samples of z> 0.8 galaxy clusters selected using different wavelength-dependent techniques and examine the physical differences between them. We consider 18 clusters from the X-ray-selected XMM Large Scale Structure (LSS) distant cluster survey and 92 clusters from the optical–mid-infrared (MIR)-selected Spitzer Adaptation of the Red Sequence Cluster survey (SpARCS) cluster survey. Both samples are selected from the same approximately 9 sq deg sky area and we examine them using common XMM–Newton, Spitizer Wide-Area Infrared Extra-galactic (SWIRE) survey, and Canada–France–Hawaii Telescope Legacy Survey data. Clusters from each sample are compared employing aperture measures of X-ray and MIR emission. We divide the SpARCS distant cluster sample into three sub-samples: (i) X-ray bright, (ii) X-ray faint, MIR bright, and (iii) X-ray faint, MIR faint clusters. We determine that X-ray- and MIR-selected clusters display very similar surface brightness distributions of galaxy MIR light. In addition, the average location and amplitude of the galaxy red sequence as measured from stacked colour histograms is very similar in the X-ray- and MIR-selected samples. The sub-sample of X-ray faint, MIR bright clusters displays a distribution of brightest cluster galaxy–barycentre position offsets which extends to higher values than all other samples. This observation indicates that such clusters may exist in a more disturbed state compared to the majority of the distant cluster population sampled by XMM–LSS and SpARCS. This conclusion is supported by stacked X-ray images for the X-ray faint, MIR bright cluster sub-sample that display weak, centrally concentrated X-ray emission, consistent with a population of growing clusters accreting from an extended envelope of material.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".