Bibliographic record
Abstract
Context. The XXL Survey is the largest homogeneous survey carried out with XMM-Newton. Covering an area of 50 deg 2 , the survey contains several hundred galaxy clusters out to a redshift of 2 above an X-ray flux limit of 5 10 -15 erg cm -2 s -1 . This paper belongs to the first series of XXL papers focusing on the bright cluster sample. Aims. We investigate the luminosity-temperature (LT) relation for the brightest clusters detected in the XXL Survey, taking fully into account the selection biases. We investigate the form of the LT relation, placing constraints on its evolution. Methods. We have classified the 100 brightest clusters in the XXL Survey based on their measured X-ray flux. These 100 clusters have been analysed to determine their luminosity and temperature to evaluate the LT relation. We used three methods to fit the form of the LT relation, with two of these methods providing a prescription to fully take into account the selection effects of the survey. We measure the evolution of the LT relation internally using the broad redshift range of the sample. Results. Taking fully into account selection effects, we find a slope of the bolometric LT relation of B LT = 3.080.15, steeper than the self-similar expectation (B LT = 2). Our best-fit result for the evolution factor is E(z) 1.640.77 , fully consistent with "strong self-similar" evolution where clusters scale self-similarly with both mass and redshift. However, this result is marginally stronger than "weak selfsimilar" evolution, where clusters scale with redshift alone. We investigate the sensitivity of our results to the assumptions made in our fitting model, finding that using an external LT relation as a low-z baseline can have a profound effect on the measured evolution. However, more clusters are needed in order to break the degeneracy between the choice of likelihood model and mass-temperature relation on the derived evolution.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".