DEMPgen: Physics event generator for Deep Exclusive Meson Production at Jefferson Lab and the EIC
Bibliographic record
Abstract
There is increasing interest in deep exclusive meson production (DEMP) reactions, as they provide access to Generalized Parton Distributions over a broad kinematic range, and are the only means of measuring pion and kaon charged electric form factors at high Q^2. Such investigations are a particularly useful tool in the study of hadronic structure in QCD's transition regime from long-distance interactions described in terms of meson-nucleon degrees of freedom, to short-distance interactions governed by hard quark-gluon degrees of freedom. To assist the planning of future experimental investigations of DEMP reactions in this transition regime, such as at Jefferson Lab and the Electron-Ion Collider (EIC), we have written a special purpose event generator, DEMPgen. Currently, DEMPgen can generate the following reactions: t-channel p(e, e'π^+)n, p(e, e'K^+)Λ[Σ^0] and n(e, e'π^-)p from a polarized 3He target. DEMPgen is modular in form, so that additional reactions can be added over time. The generator produces kinematically-complete reaction events which are absolutely-normalized, so that projected event rates can be predicted, and detector resolution requirements studied. The event normalization is based on parameterizations of theoretical models, appropriate to the kinematic regime under study. Both fixed target modes and collider beam modes are supported. This paper presents the structure of the generator, the model parameterizations used for absolute event weighting, the kinematic distributions of the generated particles, some initial results using the generator, and instructions for its use.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.025 | 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".