Cosmological parameters from combining the Lyman-α forest with CMB, galaxy clustering and SN constraints
Bibliographic record
Abstract
We combine the Ly-α forest power spectrum (LYA) from the Sloan Digital Sky Survey (SDSS) and high resolution spectra with cosmic microwave background (CMB) including three-year WMAP, and supernovae (SN) and galaxy clustering constraints to derive new constraints on cosmological parameters. The existing LYA power spectrum analysis is supplemented by constraints on the mean flux decrement derived using a principle component analysis for quasar continua, which improves the LYA constraints on the linear power. We find some tension between the WMAP3 and LYA power spectrum amplitudes, at the ∼2σ level, which is partially alleviated by the inclusion of other observations: we find σ8= 0.85 ± 0.02 compared to σ8= 0.80 ± 0.03 without LYA. For the slope, we findns= 0.965 ± 0.012. We find no evidence for the running of the spectral index in the combined analysis, dn/dlnk= −(1.5 ± 1.2) × 10−2, in agreement with inflation. The limits on the sum of neutrino masses are significantly improved: at 95% (<0.32 eV at 99.9%). This result, when combined with atmospheric and solar neutrino mixing constraints, requires that the neutrino masses cannot be degenerate,m3/m1>1.3 (95% c.l.). Assuming a thermalized fourth neutrino, we findms<0.26 eV at 95% c.l. and such a neutrino cannot be an explanation for the LSND results. In the limits of massless neutrinos, we obtain the effective number of neutrinosNνeff= 5.3−0.6+0.4−1.7+2.1−2.5+3.8andNνeff= 3.04 is allowed only at 2.4 sigma. The constraint on the dark energy equation of state isw= −1.04 ± 0.06. The constraint on curvature is Ωk= −0.003 ± 0.006. Cosmic strings limits areGμ<2.3 × 10−7at 95% c.l. and correlated isocurvature models are also tightly constrained.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".