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Record W4391879225 · doi:10.22323/1.438.0065

Comparative multi-probe study of jet energy-loss in QGP

2024· article· en· W4391879225 on OpenAlexafffund
Rouzbeh Modarresi-Yazdi, Shuzhe Shi, Charles Gale, Sangyong Jeon

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsMcGill University
FundersNuclear PhysicsOffice of ScienceAlliance de recherche numérique du CanadaNatural Sciences and Engineering Research Council of CanadaTsinghua UniversityU.S. Department of Energy
KeywordsJet (fluid)Energy (signal processing)MechanicsMaterials scienceComputer sciencePhysics

Abstract

fetched live from OpenAlex

Jet-energy loss is an important sign of the creation of Quark-Gluon Plasma in heavy-ion collisions. High transverse momentum ($p_T$) partons are produced at the moment of initial hard scattering and are modified as a result of their propagation through the created medium. We study two models of low-virtuality radiative energy loss: CUJET and MARTINI. This is done using the JETSCAPE framework, which allows for an objective comparison. CUJET is integrated into the JETSCAPE workflow, and full jet simulations, including substructure observables, are computed for the first time using leading-order DGLV rates. Strongly-interacting probes (charged hadrons, jets, jet fragmentation functions and jet shape ratio) are considered along jet-medium photons for the first time in a dynamic QGP. We find that these photons make a significant contribution in the phenomenologically interesting intermediate $p_T$ domain of $4$-$12$ GeV.

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 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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.017
GPT teacher head0.278
Teacher spread0.261 · 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 designObservational
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

Citations1
Published2024
Admission routes2
Has abstractyes

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