The Linear Theory Power Spectrum from the Lyα Forest in the Sloan Digital Sky Survey
Bibliographic record
Abstract
We analyze the SDSS Lyα forest P F ( k , z ) measurement to determine the linear theory power spectrum. Our analysis is based on fully hydrodynamic simulations, extended using hydro-particle-mesh simulations. We account for the effect of absorbers with damping wings, which leads to an increase in the slope of the linear power spectrum. We break the degeneracy between the mean level of absorption and the linear power spectrum without significant use of external constraints. We infer linear theory power spectrum amplitude Δ ( k p = 0.009 s km -1 , z p = 3.0) = 0.452 and slope n eff ( k p , z p ) = -2.321 (possible systematic errors are included through nuisance parameters in the fit: a factor ≳5 smaller errors would be obtained on both parameters if we ignored modeling uncertainties). The errors are correlated and not perfectly Gaussian, so we provide a χ 2 table to accurately describe the results. The result corresponds to σ 8 = 0.85, n = 0.94 for a ΛCDM model with Ω m = 0.3, Ω b = 0.04, and h = 0.7 but is most useful in a combined fit with the CMB. The inferred curvature of the linear power spectrum and the evolution of its amplitude and slope with redshift are consistent with expectations for ΛCDM models, with the evolution of the slope, in particular, being tightly constrained. We use this information to constrain systematic contamination, e.g., fluctuations in the UV background. This paper should serve as a starting point for more work to refine the analysis, including technical improvements such as increasing the size and number of the hydrodynamic simulations and improvements in the treatment of the various forms of feedback from galaxies and quasars.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".