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Photon radiation from heavy-ion collisions in the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.gif" overflow="scroll"><mml:msqrt><mml:mrow><mml:msub><mml:mrow><mml:mi>s</mml:mi></mml:mrow><mml:mrow><mml:mi>N</mml:mi><mml:mi>N</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:msqrt><mml:mo>=</mml:mo><mml:mn>19</mml:mn><mml:mo>−</mml:mo><mml:mn>200</mml:mn><mml:mspace width="0.25em"/><mml:mtext>GeV</mml:mtext></mml:math> regime

2019· article· lv· W2914721421 on OpenAlexafffund
Charles Gale, Sangyong Jeon, Scott McDonald, Jean-François Paquet, Chun Shen

Bibliographic record

VenueNuclear Physics A · 2019
Typearticle
Languagelv
FieldPhysics and Astronomy
TopicHigh-Energy Particle Collisions Research
Canadian institutionsMcGill University
FundersNuclear PhysicsNatural Sciences and Engineering Research Council of CanadaOffice of ScienceU.S. Department of EnergyKillam TrustsCanada Council for the ArtsMinistère de l'Économie, de la Science et de l'Innovation - QuébecCompute Canada
KeywordsPhotonPhysicsScrollCollisionThermalProduction (economics)RadiationMeasure (data warehouse)IonNuclear physicsAtomic physicsDatabaseComputer scienceOpticsMeteorologyEngineeringQuantum mechanicsComputer security

Abstract

fetched live from OpenAlex

We present calculations of prompt and thermal photon production in Au-Au collisions at sNN=19−200GeV. We discuss features of the spacetime profile of the plasma relevant for electromagnetic emission. We highlight how the suppression of prompt photon production at low sNN can provide a window to measure thermal photons in low collision energies.

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 score0.010
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.001

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 head0.017
GPT teacher head0.246
Teacher spread0.229 · 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

Citations11
Published2019
Admission routes2
Has abstractyes

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