A Semiblind Reconstruction of the History of Effective Number of Neutrinos Using CMB Data
Bibliographic record
Abstract
We explore the possibility of redshift-dependent deviations in the contribution of relativistic degrees of freedom to the radiation budget of the cosmos, conventionally parameterized by the effective number of neutrinos $N_{\rm eff}$, from the predictions of the standard model. We expand the deviations $ΔN_{\rm eff}(z)$ in terms of top-hat functions and treat their amplitudes as the free parameters of the theory to be measured alongside the standard cosmological parameters by the Planck measurements of the cosmic microwave background (CMB) anisotropies and Baryonic Acoustic Oscillations, as well as performing forecasts for futuristic CMB surveys such as PICO and CMB-S4. We reconstruct the history of $ΔN_{\rm eff}$ and find that with the current data the history is consistent with the standard scenario. Inclusion of the new degrees of freedom in the analysis increases $H_0$ to $68.71\pm 0.44$, slightly reducing the Hubble tension. With the smaller forecasted errors on the $ΔN_{\rm eff}(z)$ parametrization modes from future CMB surveys, very accurate bounds are expected within the possible range of dark radiation models.
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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 | 0.004 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".