MétaCan
Menu
Back to cohort
Record W2921493897 · doi:10.1103/physrevc.101.044904

Exploring the influence of bulk viscosity of QCD on dilepton tomography

2020· article· en· W2921493897 on OpenAlexafffund
G. Vujanovic, Jean-François Paquet, Chun Shen, Gabriel S. Denicol, Sangyong Jeon, Charles Gale, Ulrich Heinz

Bibliographic record

VenuePhysical review. C · 2020
Typearticle
Languageen
FieldPhysics and Astronomy
TopicHigh-Energy Particle Collisions Research
Canadian institutionsMcGill University
FundersOffice of Energy Research and DevelopmentNuclear PhysicsFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of CanadaConselho Nacional de Desenvolvimento Científico e TecnológicoCanada Foundation for InnovationU.S. Department of EnergyNational Science Foundation
KeywordsPhysicsLarge Hadron ColliderParticle physicsVolume viscosityQuark–gluon plasmaHadronViscosityPlasmaQuantum chromodynamicsNuclear physicsRelativistic Heavy Ion ColliderQCD matterHeavy ionIonThermodynamics

Abstract

fetched live from OpenAlex

The collective behavior of hadrons and of electromagnetic radiation in heavy-ion collisions has been widely used to study the properties of the quark-gluon plasma (QGP). Indeed this collectivity, as measured by anisotropic flow coefficients, can be used to constrain the transport properties of QGP. The goal of this contribution is to investigate the influence of the specific bulk viscosity $(\ensuremath{\zeta}/s)$ on dilepton production, both at Relativistic Heavy-Ion Collider and Large Hadron Collider energies. We explore the sensitivity of dileptons to dynamical features that bulk viscosity induces on the evolution of a strongly interacting medium, and highlight what makes them a valuable probe in the pursuit to also constrain $\ensuremath{\zeta}/s$.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

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 communication0.0010.001
Open science0.0000.001
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 head0.069
GPT teacher head0.343
Teacher spread0.275 · 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

Citations29
Published2020
Admission routes2
Has abstractyes

Explore more

Same venuePhysical review. CSame topicHigh-Energy Particle Collisions ResearchFrench-language works237,207