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Record W4324195273 · doi:10.1103/physrevc.107.034908

Comparing the martini and cujet models for jet quenching: Medium modification of jets and jet substructure

2023· article· en· W4324195273 on OpenAlexafffund
Shuzhe Shi, Rouzbeh Modarresi Yazdi, Charles Gale, Sangyong Jeon

Bibliographic record

VenuePhysical review. C · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicHigh-Energy Particle Collisions Research
Canadian institutionsMcGill University
FundersNuclear PhysicsNatural Sciences and Engineering Research Council of CanadaOffice of ScienceAlliance de recherche numérique du CanadaU.S. Department of Energy
KeywordsJet quenchingSubstructurePhysicsPlasmaQuark–gluon plasmaJet (fluid)HadronScatteringObservableNuclear physicsFragmentation (computing)Particle physicsSpectral lineCoupling (piping)MechanicsOptics

Abstract

fetched live from OpenAlex

Jets produced by the initial hard scattering in heavy ion collision events lose energy due to interactions with the color-deconfined medium formed around them: The quark-gluon plasma (QGP). Jet-medium interactions constitute an important theoretical and experimental field for studies of QGP, and various models with different assumptions have been proposed to describe them. A fair and direct comparison of these models requires that all other aspects of the simulation be fixed, which is achieved in this work by relying on the jetscape framework. We employ jetscape to directly and comprehensively compare two successful energy loss models: cujet and martini. We compare the models with the results of measurements of jet spectra and substructure observables. With the strong coupling tuned separately, we find that the two models broadly agree with each other in nuclear modification factors for charged hadrons and jets with cone size $R=0.4$. Systematic differences are reported in fragmentation functions, jet shape, and cone-size dependent jet ${R}_{AA}$.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.908
Threshold uncertainty score0.301

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.079
GPT teacher head0.387
Teacher spread0.307 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations14
Published2023
Admission routes2
Has abstractyes

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