Measurement of the bias parameter from weak lensing
Bibliographic record
Abstract
We have measured the correlation between the lensing signal induced by (dark) matter and number counts of galaxies on scales ranging from 0.15-3.0 h_{50}^{-1} Mpc (which correspond to aperture radii of 1-15 arcminutes). This provides a direct probe of the scale dependence of the ratio of the classical bias parameter b and the galaxy-mass correlation coefficient r. The results presented here are based on 16 deg^2 of R_C band data taken with the CFHT as part of the Red-Sequence Cluster Survey. We used a sample of lens galaxies with 19.5<R_C<21, and a sample of source galaxies with 21.5<R_C<24. The results are consistent with a scale independent value of b/r, which provides valuable constraints on models of galaxy formation on scales that can only be probed through weak lensing. For the currently favored cosmology (\\Omega_m=0.3, \\Omega_\\Lambda=0.7) we find b/r=1.05^{+0.12}_{-0.10}, similar to what is found on larger scales (~10 h_{50}^{-1} Mpc) from local dynamical estimates. These results support the hypothesis that light traces mass on scales ranging from 0.15 out to ~10 h_{50}^{-1} Mpc. The accuracy of the measurement will improve significantly in the coming years, enabling us to measure both b and r separately as a function of scale.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".