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Record W4404636800 · doi:10.1016/j.cpc.2024.109444

DEMPgen: Physics event generator for Deep Exclusive Meson Production at Jefferson Lab and the EIC

2024· article· en· W4404636800 on OpenAlexafffund
Z. Ahmed, Robert S. Evans, I. Goel, G. M. Huber, S. Kay, W. B. Li, L. Preet, A. Usman

Bibliographic record

VenueComputer Physics Communications · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicParticle physics theoretical and experimental studies
Canadian institutionsUniversity of Regina
FundersNatural Sciences and Engineering Research Council of CanadaScience and Technology Facilities CouncilOffice of ScienceStony Brook UniversityU.S. Department of Energy
KeywordsGenerator (circuit theory)Particle physicsPhysicsMesonEvent generatorEvent (particle physics)Production (economics)Nuclear physicsComputer scienceHadronQuantum mechanics

Abstract

fetched live from OpenAlex

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 3 He 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. PROGRAM SUMMARY Program title: DEMPgen CPC Library link to program files: https://doi.org/10.17632/3nxcm5fsdz.1 Developer's repository link: https://github.com/JeffersonLab/DEMPgen/releases Licensing provisions: GNU General Public License 3 Programming language: C++ External package: ROOT Nature of problem: Despite the increasing interest in deep exclusive meson production (DEMP) reactions, no physics event generator currently exists for these types of reactions. Such a generator is needed for the planning of future experimental investigations at Jefferson Lab and the Electron-Ion Collider (EIC), so we have undertaken to write a special purpose event generator, DEMPgen. Solution method: DEMPgen 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. The following reactions can be generated: t -channel p ( e , e ′ π + ) n , p ( e , e ′ K + ) Λ [ Σ 0 ] , and n → ( e , e ′ π − ) p from a polarized 3 He target. These reactions have been parameterized for kinematic regimes relevant to experiments at SoLID and the EIC (as described in Secs. 3.1 , 3.2 ). DEMPgen is modular in form, so that additional reactions can be added over time. Both fixed target modes and collider beam modes are supported. Additional comments including restrictions and unusual features: DEMPgen events have variable weights, which must be included to yield realistic distributions and rate projections, see Secs. 3.1.2 , 3.3.6 for more details. DEMPgen does not yet include the radiative tail contributions for exclusive lepton-hadron reactions. This is a planned future upgrade, along with other upgrades described in Sec. 5 . Developer's repository maintained by Department of Physics, University of Regina.

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How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.023
Threshold uncertainty score0.076

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0030.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0230.006

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.

Opus teacher head0.023
GPT teacher head0.294
Teacher spread0.271 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2024
Admission routes2
Has abstractyes

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