GeV-scale dark matter with <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mi>p</mml:mi></mml:math>-wave Breit-Wigner enhanced annihilation
Bibliographic record
Abstract
We consider a light scalar dark matter candidate with mass in the GeV range whose p -wave annihilation is enhanced through a Breit-Wigner resonance. The annihilation actually proceeds in the s -channel via a dark photon mediator whose mass is nearly equal to the masses of the incoming particles. We compute the temperature at which kinetic decoupling between dark matter and the primordial plasma occurs and show that including the effect of kinetic decoupling can reduce the dark matter relic density by orders of magnitude. For typical scalar masses ranging from 200 MeV to 5 GeV, we determine the range of values allowed for the dark photon couplings to the scalar and to the standard model particles after requiring the relic density to be in agreement with the value extracted from cosmological observations. We then show that μ and y -distortions of the cosmic microwave background spectrum and x-ray data from XMM-Newton strongly constrain the model and rule out the region where the dark matter annihilation cross section is strongly enhanced at small dispersion velocities. Constraints from direct detection searches and from accelerator searches for dark photons offer complementary probes of the model. Published by the American Physical Society 2024
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.005 | 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".