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Record W2244941089 · doi:10.1103/physrevc.93.044906

Production of photons in relativistic heavy-ion collisions

2016· article· en· W2244941089 on OpenAlexafffund
Jean-François Paquet, Chun Shen, Gabriel S. Denicol, Matthew Luzum, Björn Schenke, Sangyong Jeon, Charles Gale

Bibliographic record

VenuePhysical review. C · 2016
Typearticle
Languageen
FieldPhysics and Astronomy
TopicHigh-Energy Particle Collisions Research
Canadian institutionsMcGill University
FundersFP7 People: Marie-Curie ActionsFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of CanadaOffice of ScienceCanada Foundation for InnovationEmberi Eroforrások MinisztériumaCompute CanadaSeventh Framework ProgrammeMcGill UniversityU.S. Department of Energy
KeywordsPhotonPhysicsAnisotropyMomentum (technical analysis)Nuclear physicsQuantum electrodynamicsThermalShear (geology)Work (physics)Computational physicsQuantum mechanicsThermodynamicsMaterials science

Abstract

fetched live from OpenAlex

In this work it is shown that the use of a hydrodynamical model of heavy-ion collisions which incorporates recent developments, together with updated photon emission rates, greatly improves agreement with both ALICE and PHENIX measurements of direct photons, supporting the idea that thermal photons are the dominant source of direct photon momentum anisotropy. The event-by-event hydrodynamical model uses the impact parameter dependent Glasma model (IP-Glasma) initial states and includes, for the first time, both shear and bulk viscosities, along with second-order couplings between the two viscosities. The effect of both shear and bulk viscosities on the photon rates is studied, and those transport coefficients are shown to have measurable consequences on the photon momentum anisotropy.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
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 head0.025
GPT teacher head0.358
Teacher spread0.333 · 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 designBench or experimental
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

Citations348
Published2016
Admission routes2
Has abstractyes

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