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Record W2518899115 · doi:10.1103/physrevc.95.014906

Collectivity and electromagnetic radiation in small systems

2017· article· en· W2518899115 on OpenAlexafffund
Chun Shen, Jean-François Paquet, Gabriel S. Denicol, Sangyong Jeon, Charles Gale

Bibliographic record

VenuePhysical review. C · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicHigh-Energy Particle Collisions Research
Canadian institutionsMcGill University
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of CanadaCanada Council for the ArtsCanada Foundation for InnovationU.S. Department of Energy
KeywordsPhysicsHadronLarge Hadron ColliderObservableNuclear physicsRelativistic Heavy Ion ColliderPhotonParticle physicsProtonIonHeavy ion

Abstract

fetched live from OpenAlex

Collective behavior has been observed in hadronic measurements of high multiplicity $\text{proton}+\text{lead}$ collisions at the Large Hadron Collider, as well as in (proton, deuteron, helium-3) + gold collisions at the Relativistic Heavy Ion Collider. To better understand the evolution dynamics and the properties of the matter created in these small systems, a systematic study of the soft hadronic observables together with electromagnetic radiation from these collisions is performed by using a hydrodynamic framework. Quantitative agreement is found between theoretical calculations and existing experimental hadronic observables. The validity of the fluid-dynamical description is estimated by calculating Knudsen and inverse Reynolds numbers. Sizeable thermal yields are predicted for low-${p}_{T}$ photons. Further predictions of higher-order charged hadron anisotropic flow coefficients and of thermal photon enhancement are proposed.

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)0.001
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
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
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 head0.022
GPT teacher head0.351
Teacher spread0.329 · 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

Citations116
Published2017
Admission routes2
Has abstractyes

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