Simulating glueball production in <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:msub><mml:mi>N</mml:mi><mml:mi>f</mml:mi></mml:msub><mml:mo>=</mml:mo><mml:mn>0</mml:mn></mml:math> QCD
Bibliographic record
Abstract
In an $SU({N}_{c})$ gauge theory with zero light quark flavors ${N}_{f}=0$, the only hadronic states that form below the confinement scale are composite gluon states called glueballs. These minimal confining sectors arise in many hidden valley extensions of the Standard Model, including scenarios that could hold the solution to the dark matter question and the hierarchy problem. Quantitative study of dark glueball phenomenology requires an understanding of pure glue hadronization, which to date is severely lacking. In this work we show that significant progress can be made by combining a perturbative pure glue parton shower with a self-consistent and physically motivated parametrization of the unknown nonperturbative physics, thanks to the modest hierarchy between the glueball mass and the confinement scale. We make our simulation code available as the public glueshower package, the first glueball generator for hidden valley theories, and perform preliminary studies of several glueball production observables, with theoretical uncertainties that take the full range of possible hadronization scenarios into account. We hope this will enable new studies of dark sector phenomenology that were previously inaccessible.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".