ETHOS – an effective theory of structure formation: dark matter physics as a possible explanation of the small-scale CDM problems
Bibliographic record
Abstract
We present the first simulations within an effective theory of structure formation (ETHOS), which includes the effect of interactions between dark matter and dark radiation on the linear initial power spectrum and dark matter self-interactions during non-linear structure formation. We simulate a Milky Way-like halo in four different dark matter models and the cold dark matter case. Our highest resolution simulation has a particle mass of 2.8 × 104 M⊙ and a softening length of 72.4 pc. We demonstrate that all alternative models have only a negligible impact on large-scale structure formation. On galactic scales, however, the models significantly affect the structure and abundance of subhaloes due to the combined effects of small-scale primordial damping in the power spectrum and late-time self-interactions. We derive an analytic mapping from the primordial damping scale in the power spectrum to the cutoff scale in the halo mass function and the kinetic decoupling temperature. We demonstrate that certain models within this extended effective framework that can alleviate the too-big-to-fail and missing satellite problems simultaneously, and possibly the core-cusp problem. The primordial power spectrum cutoff of our models naturally creates a diversity in the circular velocity profiles, which is larger than that found for cold dark matter simulations. We show that the parameter space of models can be constrained by contrasting model predictions to astrophysical observations. For example, some models may be challenged by the missing satellite problem if baryonic processes were to be included and even oversolve the too-big-to-fail problem; thus ruling them out.
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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 | 0.001 | 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".