SCALING RELATIONS AND OVERABUNDANCE OF MASSIVE CLUSTERS AT<i>z</i>≳ 1 FROM WEAK-LENSING STUDIES WITH THE<i>HUBBLE SPACE TELESCOPE</i>
Bibliographic record
Abstract
We present weak gravitational lensing analysis of 22 high-redshift ( z ≳ 1) clusters based on Hubble Space Telescope images. Most clusters in our sample provide significant lensing signals and are well detected in their reconstructed two-dimensional mass maps. Combining the current results and our previous weak-lensing studies of five other high- z clusters, we compare gravitational lensing masses of these clusters with other observables. We revisit the question whether the presence of the most massive clusters in our sample is in tension with the current ΛCDM structure formation paradigm. We find that the lensing masses are tightly correlated with the gas temperatures and establish, for the first time, the lensing mass–temperature relation at z ≳ 1. For the power-law slope of the M – T X relation ( M ∝ T α ), we obtain α = 1.54 ± 0.23. This is consistent with the theoretical self-similar prediction α = 3/2 and with the results previously reported in the literature for much lower redshift samples. However, our normalization is lower than the previous results by 20%–30%, indicating that the normalization in the M – T X relation might evolve. After correcting for Eddington bias and updating the discovery area with a more conservative choice, we find that the existence of the most massive clusters in our sample still provides a tension with the current ΛCDM model. The combined probability of finding the four most massive clusters in this sample after the marginalization over cosmological parameters is less than 1%.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".