RCSLenS: a new estimator for large-scale galaxy–matter correlations
Bibliographic record
Abstract
We present measurements of the galaxy bias b and the galaxy–matter cross-correlation coefficient r for the Baryon Oscillation Spectroscopic Survey LOWZ luminous red galaxy sample. Using a new statistical weak lensing analysis of the Red Cluster Sequence Lensing Survey (RCSLenS), we find the bias properties of this sample to be higher than previously reported with |$b=2.45_{-0.05}^{+0.05}$| and |$r=1.64_{-0.16}^{+0.17}$| on scales between 3 and 20 arcmin. We repeat the measurement for angular scales of 20 arcmin ≤ ϑ ≤ 70 arcmin, which yields |$b=2.39_{-0.07}^{+0.07}$| and |$r=1.24_{-0.25}^{+0.26}$|. This is the first application of a data compression analysis using a complete set of discrete estimators for galaxy–galaxy lensing and galaxy clustering. As cosmological data sets grow, our new method of data compression will become increasingly important in order to interpret joint weak lensing and galaxy clustering measurements and to estimate the data covariance. In future studies, this formalism can be used as a tool to study the large-scale structure of the Universe to yield a precise determination of cosmological parameters.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".