Lepton-flavored asymmetric dark matter and interference in direct detection
Bibliographic record
Abstract
In flavored dark matter models, dark matter can scatter off of nuclei through Higgs and photon exchange, both of which can arise from renormalizable interactions and individually lead to strong constraints from direct detection. While these two interaction channels can destructively interfere in the scattering amplitude, for a thermal relic with equal abundances for the dark matter particle and its antiparticle, this produces no effect on the total event rate. Focusing on lepton-flavored dark matter, we show that it is quite natural for dark matter to have become asymmetric during high-scale leptogenesis, and that in this case the direct detection bounds can be significantly weakened due to interference. We quantify this by mapping out and comparing the regions of parameter space that are excluded by direct detection for the symmetric and asymmetric cases of lepton-flavored dark matter. In particular, we show that the entire parameter region except for a narrow Higgs resonance window is ruled out in the symmetric case for fermion dark matter when the coupling to the Higgs dominates over the coupling to leptons, while large portions of parameter space are still allowed for the asymmetric case. The same is also true for a dark matter mass above 8 GeV for scalar dark matter when the coupling to leptons dominates over the coupling to the Higgs.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".