Bibliographic record
Abstract
We present weak-lensing mass measurements of 50 X-ray luminous galaxy clusters at $0.15\le z\le0.3$, based on high quality observations with Suprime-Cam mounted on the 8.2-m Subaru telescope. We pay close attention to possible systematic biases, aiming to control them at the $\lt4$ per cent level. The dominant source of systematic bias in weak-lensing measurements of the mass of individual galaxy clusters is contamination of background galaxy catalogues by faint cluster and foreground galaxies. We extend our conservative method for selecting background galaxies with $(V-i')$ colours redder than the red sequence of cluster members to use a colour-cut that depends on cluster-centric radius. This allows us to define background galaxy samples that suffer $\le1$ per cent contamination, and comprise $13$ galaxies per square arcminute. Thanks to the purity of our background galaxy catalogue, the largest systematic in our measurement is a shape measurement bias of $3$ per cent, that we measure using custom-made simulations that probe weak shears upto $g=0.3$. Our individual cluster mass and concentration measurements are in excellent agreement with predictions of the mass-concentration relation. Equally, our stacked shear profile is in excellent agreement with the Navarro Frenk and White profile. Our new LoCuSS mass measurements are consistent with the CCCP and CLASH surveys, and in tension with the Weighing the Giants (WtG) at $\sim2\sigma$ significance.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".