Indirect detection of Dark Matter annihilating into Dark Glueballs
Bibliographic record
Abstract
Abstract We examine indirect detection of dark matter that annihilates into dark glueballs, which in turn decay into the Standard Model via a range of portals. This arises if the dark matter candidate couples to a confining gauge force without light flavours, representative of many possible complex dark sectors. Such Hidden Valley scenarios are being increasingly considered due to non-detection of minimal models as well as theoretical motivations such as the Twin Higgs solution to the little hierarchy problem. Study of dark glueballs in indirect detection has previously been hampered by the difficulty of modeling their production in dark showers. We use the recent GlueShower code to produce the first constraints on dark matter annihilating via dark glueballs into the Standard Model across photon, antiproton, and positron channels. We also fit the Galactic Centre Excess and use this observation, combined with other astrophysical constraints, to show how multi-channel observations can constrain UV and IR details of the theory, namely the exact decay portal and hadronization behaviour respectively. This provides unique complementary discovery and diagnostic potential to Hidden Valley searches at colliders. It is interesting to note that thermal WIMPs annihilating to $$ \mathcal{O} $$ O (10 GeV) dark glueballs and then the SM via the Twin-Higgs-like decay portal can account for the GCE while respecting other constraints.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".