Reducing the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mrow><mml:msub><mml:mrow><mml:mi>H</mml:mi></mml:mrow><mml:mrow><mml:mn>0</mml:mn></mml:mrow></mml:msub></mml:mrow></mml:math> and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mrow><mml:msub><mml:mrow><mml:mi>σ</mml:mi></mml:mrow><mml:mrow><mml:mn>8</mml:mn></mml:mrow></mml:msub></mml:mrow></mml:math> tensions with dark matter-neutrino interactions
Bibliographic record
Abstract
The introduction of dark matter-neutrino interactions modifies the cosmic microwave background (CMB) angular power spectrum at all scales, thus affecting the reconstruction of the cosmological parameters. Such interactions can lead to a slight increase of the value of ${H}_{0}$ and a slight decrease of ${S}_{8}\ensuremath{\equiv}{\ensuremath{\sigma}}_{8}\sqrt{{\mathrm{\ensuremath{\Omega}}}_{m}/0.3}$, which can help reduce somewhat the tension between the CMB and weak lensing or Cepheids data sets. Here we show that it is impossible to solve both tensions simultaneously. While the 2015 Planck temperature and low multipole polarization data combined with the Cepheids data sets prefer large values of the Hubble rate (up to ${H}_{0}={72.1}_{\ensuremath{-}1.7}^{+1.5}\text{ }\text{ }\mathrm{km}/\mathrm{s}/\mathrm{Mpc}$, when ${N}_{\mathrm{eff}}$ is free to vary), the ${\ensuremath{\sigma}}_{8}$ parameter remains too large to reduce the ${\ensuremath{\sigma}}_{8}$ tension. Adding high multipole Planck polarization data does not help since this data shows a strong preference for low values of ${H}_{0}$, thus worsening current tensions, even though they also prefer smaller value of ${\ensuremath{\sigma}}_{8}$.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.077 | 0.037 |
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".