Bibliographic record
Abstract
Abstract Exotic dark matter (EDM) refers to a dark matter species whose equation of state deviates from zero at late times. This behavior enables it to model a variety of non-standard late-time cosmologies, offering alternatives to various dark energy (DE) models, especially when the DE sector violates the null energy condition. In this work, by fitting to a compressed CMB likelihood, BAO, and Supernovae (SNe) data and comparing models in a Bayesian approach, we show that simple models of exotic dark matter are statistically comparable to the w 0 w a CDM DE model in explaining the recent anomaly in the late-time cosmological evolution suggested by DESI and supernova observations, although in both classes of models the evidence against the ΛCDM model only appears when the DES-Y5 or Union3 SNe dataset is included. The value of H 0 remains similar to that in the DE model, except in the no-SNe case, where the DE model predicts lower values than ΛCDM, thereby worsening the Hubble tension, whereas the EDM models yield values closer to that of ΛCDM, albeit with larger uncertainty. In addition, the EDM models predict a drastically different energy budget for the present-day universe compared to the standard model, and provide an explanation for a coincidence problem in the DE-model explanation of the DESI anomaly.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".