The clustering of galaxies in the SDSS-III Baryon Oscillation Spectroscopic Survey: signs of neutrino mass in current cosmological data sets
Bibliographic record
Abstract
We investigate the cosmological implications of the latest growth of structure measurement from the Baryon Oscillation Spectroscopic Survey (BOSS) CMASS Data Release 11 with particular focus on the sum of the neutrino masses, ∑mν. We examine the robustness of the cosmological constraints from the baryon acoustic oscillation (BAO) scale, the Alcock–Paczynski effect and redshift-space distortions (DV/rs, FAP, fσ8) of Beutler et al., when introducing a neutrino mass in the power spectrum template. We then discuss how the neutrino mass relaxes discrepancies between the cosmic microwave background (CMB) and other low-redshift measurements within Λ cold dark matter. Combining our cosmological constraints with 9-year Wilkinson Microwave Anisotropy Probe (WMAP9) yields ∑mν = 0.36 ± 0.14 eV (68 per cent c.l.), which represents a 2.6σ preference for non-zero neutrino mass. The significance can be increased to 3.3σ when including weak lensing results and other BAO constraints, yielding ∑mν = 0.35 ± 0.10 eV (68 per cent c.l.). However, combining CMASS with Planck data reduces the preference for neutrino mass to ∼2σ. When removing the CMB lensing effect in the Planck temperature power spectrum (by marginalizing over AL), we see shifts of ∼1σ in σ8 and Ωm, which have a significant effect on the neutrino mass constraints. In the case of CMASS plus Planck without the AL lensing signal, we find a preference for a neutrino mass of ∑mν = 0.34 ± 0.14 eV (68 per cent c.l.), in excellent agreement with the WMAP9+CMASS value. The constraint can be tightened to 3.4σ yielding ∑mν = 0.36 ± 0.10 eV (68 per cent c.l.) when weak lensing data and other BAO constraints are included.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".