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Rapidity-dependent jet energy loss in small systems with finite-size effects and running coupling

2017· article· en· W2596883840 on OpenAlexafffund
Chanwook Park, Chun Shen, Sangyong Jeon, Charles Gale

Bibliographic record

VenueNuclear and Particle Physics Proceedings · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicHigh-Energy Particle Collisions Research
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaOffice of ScienceMcGill UniversityKillam TrustsFonds de recherche du Québec – Nature et technologiesCanada Council for the ArtsCompute Canada
KeywordsJet quenchingPhysicsRapidityElliptic flowMultiplicity (mathematics)Nuclear physicsJet (fluid)Energy flowParticle physicsCoupling (piping)ThermalCoupling lossEnergy (signal processing)MechanicsQuark–gluon plasmaQuarkHadronHeavy ionThermodynamics

Abstract

fetched live from OpenAlex

Longitudinal dynamics of particle production and rapidity-dependent jet energy loss are investigated in small and asymmetric colliding systems. We utilize an improved version of martini in which two improvements are implemented to calculate the effect of the strongly coupled QGP droplet on jet energy loss. We show that those realistic prescriptions improve the results of nuclear modification factor calculations. We also observe visible energy loss of jets in a thermal background of high-multiplicity p-Pb collisions, and a clear correlation between the energy loss and elliptic flow coefficients for energetic particles. We conclude that systematic measurements of jet quenching in central collisions of small systems can support the formation of the QGP droplet.

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)0.001
metaresearch head score (Gemma)0.002
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.018
GPT teacher head0.249
Teacher spread0.231 · 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

Citations20
Published2017
Admission routes2
Has abstractyes

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Same venueNuclear and Particle Physics ProceedingsSame topicHigh-Energy Particle Collisions ResearchFrench-language works237,207