Testing gravity with galaxy-galaxy lensing and redshift-space distortions using CFHT-Stripe 82, CFHTLenS, and BOSS CMASS datasets
Bibliographic record
Abstract
The combination of galaxy-galaxy lensing (GGL) and redshift space distortion of galaxy clustering (RSD) is a privileged technique to test general relativity predictions and break degeneracies between the growth rate of structure parameterfand the amplitude of the linear power spectrumσ8. We performed a joint GGL and RSD analysis on 250 sq. deg using shape catalogues from CFHTLenS and CFHT-Stripe 82 and spectroscopic redshifts from the BOSS CMASS sample. We adjusted a model that includes non-linear biasing, RSD, and Alcock–Paczynski effects. We used an N-body simulation supplemented by an abundance matching prescription for CMASS galaxies to build a set of overlapping lensing and clustering mocks. Together with additional spectroscopic data, this helps us to quantify and correct several systematic errors, such as photometric redshifts. We findf(z = 0.57) = 0.95 ± 0.23,σ8(z = 0.57) = 0.55 ± 0.07 and Ωm = 0.31 ± 0.08, in agreement withPlanckcosmological results 2018. We also estimate the probe of gravityEG = 0.43 ± 0.10, in agreement with ΛCDM−GR predictions ofEG = 0.40. This analysis reveals that RSD efficiently decreases the GGL uncertainty on Ωmby a factor of 4 and by 30% onσ8. We make our mock catalogues available on the Skies and Universe database.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".