LoCuSS: First Results from Strong-lensing Analysis of 20 Massive Galaxy Clusters at z~0.2
Bibliographic record
Abstract
We present a statistical analysis of a sample of 20 strong lensing clusters drawn from the Local Cluster Substructure Survey (LoCuSS), based on high resolution Hubble Space Telescope imaging of the cluster cores and follow-up spectroscopic observations using the Keck-I telescope. We use detailed parameterized models of the mass distribution in the cluster cores, to measure the total cluster mass and fraction of that mass associated with substructures within R<250kpc.These measurements are compared with the distribution of baryons in the cores, as traced by the old stellar populations and the X-ray emitting intracluster medium. Our main results include: (i) the distribution of Einstein radii is log-normal, with a peak and 1sigma width of =1.16+/-0.28; (ii) we detect an X-ray/lensing mass discrepancy of =1.3 at 3 sigma significance -- clusters with larger substructure fractions displaying greater mass discrepancies, and thus greater departures from hydrostatic equilibrium; (iii) cluster substructure fraction is also correlated with the slope of the gas density profile on small scales, implying a connection between cluster-cluster mergers and gas cooling. Overall our results are consistent with the view that cluster-cluster mergers play a prominent role in shaping the properties of cluster cores, in particular causing departures from hydrostatic equilibrium, and possibly disturbing cool cores. Our results do not support recent claims that large Einstein radius clusters present a challenge to the CDM paradigm.
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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 | 0.003 |
| 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 | 0.001 |
| 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".