Bibliographic record
Abstract
Self-interacting dark matter offers an interesting alternative to collisionless dark matter because of its ability to preserve the large-scale success of the cold dark matter model, while seemingly solving its challenges on small scales. We present here the first study of the expected dark matter detection signal in a fully cosmological context taking into account different self-scattering models for dark matter. We demonstrate that models with constant and velocity-dependent cross-sections, which are consistent with observational constraints, lead to distinct signatures in the velocity distribution, because non-thermalized features found in the cold dark matter distribution are thermalized through particle scattering. Depending on the model, self-interaction can lead to a 10 per cent reduction of the recoil rates at high energies, corresponding to a minimum speed that can cause recoil larger than 300 km s−1, compared to the cold dark matter case. At lower energies these differences are smaller than 5 per cent for all models. The amplitude of the annual modulation signal can increase by up to 25 per cent, and the day of maximum amplitude can shift by about two weeks with respect to the cold dark matter expectation. Furthermore, the exact day of phase reversal of the modulation signal can also differ by about a week between the different models. In general, models with velocity-dependent cross-sections peaking at the typical velocities of dwarf galaxies lead only to minor changes in the detection signals, whereas allowed constant cross-section models lead to significant changes. We conclude that different self-interacting dark matter scenarios might be distinguished from each other through the details of direct detection signals. Furthermore, detailed constraints on the intrinsic properties of dark matter based on null detections should take into account the possibility of self-scattering and the resulting effects on the detector signal.
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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) | 0.000 | 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".