Addressing the Hubble and S <sub>8</sub> tensions with a kinetically mixed dark sector
Bibliographic record
Abstract
Abstract We present a kinetically mixed dark sector (KMIX) model to address the Hubble and S 8 tensions. Inspired from string theory, our model includes two fields: an axion, which plays a role similar to the scalar field in early dark energy models, and a dilaton. This theory differs from other axio-dilaton models aimed at the Hubble tension in that there is necessarily kinetic mixing between the two fields which allows for efficient energy transfer from the axion into the dilaton which has w ≈ 1. As a direct consequence of these dynamics, we find the model does not need to resort to a fine-tuned potential to solve the Hubble tension and naturally accommodates a standard axion potential. Furthermore, the axion will necessarily makeup a small (fuzzy) fraction of Ωcdm once it begins to oscillate at the bottom of its potential and will suppress the growth of perturbations on scales sensitive to S 8. Interestingly, the scale of the potential for the dilaton has to be small, ≲ 𝒪(10 meV)4, suggesting the possibility for a connection to dark energy. Implementing the dynamics for the background and perturbations in a modified Boltzmann code we calculate the CMB and matter power spectra for our theory. Exploring the parameter space of our model, we find regions which can accommodate a ∼ 10% increase in H 0 from the Planck inferred value and S 8 values that are consistent with large-scale structure constraints.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".