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Radiative and Collisional Jet Energy Loss in the Quark-Gluon Plasma at the BNL Relativistic Heavy Ion Collider

2008· article· en· W1504602297 on OpenAlexaff
Guang-You Qin, Jörg Ruppert, Charles Gale, Sangyong Jeon, Guy D. Moore, Munshi G. Mustafa

Bibliographic record

VenuePhysical Review Letters · 2008
Typearticle
Languageen
FieldPhysics and Astronomy
TopicHigh-Energy Particle Collisions Research
Canadian institutionsMcGill University
Fundersnot available
KeywordsPhysicsJet quenchingRelativistic Heavy Ion ColliderPartonNuclear physicsBremsstrahlungQuark–gluon plasmaPlasmaJet (fluid)Radiative transferGluonParticle physicsColliderAtomic physicsIonQuarkHeavy ion

Abstract

fetched live from OpenAlex

We calculate and compare bremsstrahlung and collisional energy loss of hard partons traversing a quark-gluon plasma. Our treatment of both processes is complete at leading order in the coupling and accounts for the probabilistic nature of the jet energy loss. We find that the nuclear modification factor R(AA) for neutral pi(0) production in heavy ion collisions is sensitive to the inclusion of collisional and radiative energy loss contributions while the averaged energy loss only slightly increases if collisional energy loss is included for parent parton energies E>>T. These results are important for the understanding of jet quenching in Au+Au collisions at 200A GeV at the Relativistic Heavy Ion Collider (RHIC). Comparison with data is performed applying the energy loss calculation to a relativistic ideal (3+1)-dimensional hydrodynamic description of the thermalized medium formed at RHIC.

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 score0.007
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
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 head0.298
Teacher spread0.276 · 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

Citations216
Published2008
Admission routes1
Has abstractyes

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