Cosmological constraints from galaxy–lensing cross-correlations using BOSS galaxies with SDSS and CMB lensing
Bibliographic record
Abstract
ABSTRACT We present cosmological parameter constraints based on a joint modelling of galaxy–lensing cross-correlations and galaxy clustering measurements in the SDSS, marginalizing over small-scale modelling uncertainties using mock galaxy catalogues, without explicit modelling of galaxy bias. We show that our modelling method is robust to the impact of different choices for how galaxies occupy dark matter haloes and to the impact of baryonic physics (at the $\sim 2{{\ \rm per\ cent}}$ level in cosmological parameters) and test for the impact of covariance on the likelihood analysis and of the survey window function on the theory computations. Applying our results to the measurements using galaxy samples from BOSS and lensing measurements using shear from SDSS galaxies and CMB lensing from Planck, with conservative scale cuts, we obtain $S_8\equiv \left(\frac{\sigma _8}{0.8228}\right)^{0.8}\left(\frac{\Omega _\mathrm{ m}}{0.307}\right)^{0.6}=0.85\pm 0.05$ (stat.) using LOWZ × SDSS galaxy lensing, and S8 = 0.91 ± 0.1 (stat.) using combination of LOWZ and CMASS × Planck CMB lensing. We estimate the systematic uncertainty in the galaxy–galaxy lensing measurements to be $\sim 6{{\ \rm per\ cent}}$ (dominated by photometric redshift uncertainties) and in the galaxy–CMB lensing measurements to be $\sim 3{{\ \rm per\ cent}}$, from small-scale modelling uncertainties including baryonic physics.
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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 | 0.009 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".