Multimediator models for the Galactic Center gamma ray excess
Bibliographic record
Abstract
Tentative evidence for excess GeV-scale gamma rays from the Galactic Center has been corroborated by several groups, including the Fermi collaboration, on whose data the observation is based. Dark matter annihilation into standard model particles has been shown to give a good fit to the signal for a variety of final state particles, but models with heavy mediators are typically inconsistent with constraints from direct detection or monojets. Models where the dark matter annihilates to mediators that subsequently decay are less constrained. We perform global fits of such models to recent data, allowing branching fractions to all possible fermionic final states to vary. The best fit models, including constraints from the AMS-02 experiment (and also antiproton ratio), require branching primarily to muons, with a $\ensuremath{\sim}10--20%$ admixture of $b$ quarks, and no other species. This suggests models in which there are two scalar mediators that mix with the Higgs, and have masses consistent with such a decay pattern. The scalar that decays to ${\ensuremath{\mu}}^{+}{\ensuremath{\mu}}^{\ensuremath{-}}$ must therefore be lighter than $2{m}_{\ensuremath{\tau}}\ensuremath{\cong}3.6\text{ }\text{ }\mathrm{GeV}$. Such a small mass can cause Sommerfeld enhancement, which is useful to explain why the best-fit annihilation cross section is larger than the value needed for a thermal relic density. For light mediator masses (0.2--2) GeV, it can also naturally lead to elastic dark matter self-interactions at the right level for addressing discrepancies in small structure formation as predicted by collisionless cold dark matter.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.033 | 0.006 |
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".