Lyman-$\alpha$ forest constraints on interacting dark sectors
Bibliographic record
Abstract
The Lyman-$\alpha$ forest is a valuable probe of dark matter models featuring a scale-dependent suppression of the power spectrum as compared to $\Lambda$CDM. In this work, we present a new estimator of the Lyman-$\alpha$ flux power spectrum that does not rely on hydrodynamical simulations. Our framework is characterized by nuisance parameters that encapsulate the complex physics of the intergalactic medium and sensitivity to highly non-linear small-scale modes. After validating the approach based on high-resolution hydrodynamical simulations for $\Lambda$CDM, we derive conservative constraints on interacting dark matter models from BOSS Lyman-$\alpha$ data on large scales, k<0.02(km/s)^(-1), with the relevant nuisance parameters left free in the model fit. The estimator yields lower bounds on the mass of cannibal dark matter, where freeze-out occurs through 3-to-2 annihilation, in the MeV range. Furthermore, we find that models of dark matter interacting with dark radiation, which have been argued to address the $H_0$ and $\sigma_8$ tensions, are compatible with BOSS Lyman-$\alpha$ data.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".