Searching for leptophilic composite asymmetric dark sector at <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:msup> <mml:mi>e</mml:mi> <mml:mo>+</mml:mo> </mml:msup> <mml:msup> <mml:mi>e</mml:mi> <mml:mo>−</mml:mo> </mml:msup> </mml:math> colliders
Bibliographic record
Abstract
Composite asymmetric dark matter (ADM) models provide a well-motivated paradigm that simultaneously explains dark matter (DM) relic density and matter-antimatter asymmetry. In these models, the mass of the DM candidate (the lightest dark baryon) is generated through the dark confinement scale dynamics. Although the leptophilic composite ADM model offers a viable framework, comprehensive studies of its collider phenomenology are absent. This work systematically explores novel signatures from the leptophilic composite asymmetric dark sector at both low-energy and high-energy e + e − colliders as well as other existing collider constraints. We demonstrate detectability of TeV-scale mediators along with sub-GeV to GeV-scale lightest dark mesons at Belle II and its proposed far detector, GAZELLE, as well as Circular Electron Positron Collider/Future Circular Collider-electron-positron experiments. Moreover, these experiments exhibit complementary coverage of the model parameter space.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".