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Record W2004679117 · doi:10.1103/physrevc.84.064901

Viscous photons in relativistic heavy ion collisions

2011· article· en· W2004679117 on OpenAlexaff
Maxime Dion, Jean-François Paquet, Björn Schenke, Clint Young, Sangyong Jeon, Charles Gale

Bibliographic record

VenuePhysical Review C · 2011
Typearticle
Languageen
FieldPhysics and Astronomy
TopicHigh-Energy Particle Collisions Research
Canadian institutionsMcGill University
FundersU.S. Department of Energy
KeywordsPhysicsHeavy ionPhotonNuclear physicsIonAtomic physicsQuantum mechanics

Abstract

fetched live from OpenAlex

Theoretical studies of the production of real thermal photons in relativistic heavy ion collisions at the Relativistic Heavy Ion Collider (RHIC) are performed. The space-time evolution of the colliding system is modelled using music, a 3+1D relativistic hydrodynamic simulation, using both its ideal and viscous versions. The inclusive spectrum and its azimuthal angular anisotropy are studied separately, and the relative contributions of the different photon sources are highlighted. It is shown that the photon ${v}_{2}$ coefficient is especially sensitive to the details of the microscopic dynamics like the equation of state, the ratio of shear viscosity over entropy density, $\ensuremath{\eta}/s$, and to the morphology of the initial state.

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 score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.047
GPT teacher head0.348
Teacher spread0.302 · 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

Citations94
Published2011
Admission routes1
Has abstractyes

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