The Redshift Dependence of the Alcock–Paczynski Effect: Cosmological Constraints from the Current and Next Generation Observations
Bibliographic record
Abstract
Abstract The tomographic Alcock–Paczynski (AP) test is a robust large-scale structure (LSS) measurement that receives little contamination from the redshift space distortion. It has placed tight cosmological constraints by using small and intermediate clustering scales of the LSS data. However, previous works have neglected the cross-correlation among different redshift bins, which could cause the statistical uncertainty being underestimated by ∼20%. In this work, we further improve this method by including this multi-redshift’s full correlation. We apply it to the SDSS DR12 galaxies sample and find out that, for ΛCDM, the combination of AP with the Planck+BAO data set slightly reduces (within 1σ) Ω m to 0.304 ± 0.007 (68.3% CL). This then leads to a larger H 0 and also mildly affects Ω b h 2 and n s as well as the derived parameters z *, r *, and z re but not τ, A s , and σ 8. For the flat wCDM model, our measurement gives Ω m = 0.301 ± 0.010 and w = −1.090 ± 0.047, where the additional AP measurement reduces the error budget by ∼25%. When including more parameters into the analysis, the AP method also improves the constraints on Ω k , , and N eff by 20%–30%. Early universe parameters such as and r, however, are unaffected. Assuming the dark energy equation of state , the Planck+BAO+SNe Ia+H 0+AP data sets prefer a dynamical dark energy at ≈1.5σ CL. Finally, we forecast the cosmological constraints expected from the DESI galaxy survey and find that combining AP with the CMB+BAO method would improve the w 0–w a constraint by a factor of ∼10.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".