The Weak Lensing Signal and the Clustering of BOSS Galaxies I: Measurements
Bibliographic record
Abstract
A joint analysis of the clustering of galaxies and their weak gravitational lensing signal is well-suited to simultaneously constrain the galaxy–halo connection as well as the cosmological parameters by breaking the degeneracy between galaxy bias and the amplitude of clustering signal. In a series of two papers, we perform such an analysis at the highest redshift () in the literature using CMASS galaxies in the Sloan Digital Sky Survey-III Baryon Oscillation Spectroscopic Survey Eleventh Data Release (BOSS DR11) catalog spanning 8300 deg2. In this paper, we present details of the clustering and weak lensing measurements of these galaxies. We define a subsample of 400,916 CMASS galaxies based on their redshifts and stellar-mass estimates so that the galaxies constitute an approximately volume-limited and similar population over the redshift range . We obtain a signal-to-noise ratio (S/N) for the galaxy clustering measurement. We also explore the redshift and stellar-mass dependence of the clustering signal. For the weak lensing measurement, we use existing deeper imaging data from the Canada–France–Hawaii Telescope Legacy Survey with publicly available shape and photometric redshift catalogs from CFHTLenS, but only in a 105 deg2 area that overlaps with BOSS. This restricts the lensing measurement to only 5084 CMASS galaxies. After careful systematic tests, we find a highly significant detection of the CMASS weak lensing signal, with total S/N . These measurements form the basis of the halo occupation distribution and cosmology analysis presented in More et al. (Paper II).
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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 | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".