Constraints on Massive Neutrinos from the CFHTLS Angular Power Spectrum
Bibliographic record
Abstract
We use the galaxy angular power spectrum at $z\sim0.5-1.2$ from the Canada-France-Hawaii-Telescope Legacy Survey Wide fields (CFHTLS-Wide) to constrain separately the total neutrino mass $\sum{m_ν}$ and the effective number of neutrino species $N_{\rm{eff}}$. This survey has recently benefited from an accurate calibration of the redshift distribution, allowing new measurements of the (non-linear) matter power spectrum in a unique range of scales and redshifts sensitive to neutrino free streaming. Our analysis makes use of a recent model for the effect of neutrinos on the weakly non-linear matter power spectrum derived from accurate N-body simulations. We show that CFHTLS, combined with WMAP7 and a prior on the Hubble constant provides an upper limit of $\sum{m_ν}<0.29\,$eV and $N_{\rm{eff}} =4.17^{+1.62}_{-1.26}$ (2$\,σ$ confidence levels). If we omit smaller scales which may be affected by non-linearities, these constraints become $\sum{m_ν}<0.41\,$eV and $N_{\rm{eff}} =3.98^{+2.02}_{-1.20}$ (2$\,σ$ confidence levels). Finally we show that the addition of other large scale structures probes can further improve these constraints, demonstrating that high redshift large volumes surveys such as CFHTLS are complementary to other cosmological probes of the neutrino mass.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".