MétaCan
Menu
Back to cohort
Record W4385341427 · doi:10.1103/physrevc.108.014911

Quenching jets increases their flavor

2023· article· en· W4385341427 on OpenAlexafffund
C. Sirimanna, Ismail Soudi, G. Vujanovic, Wenjing Xing, Shanshan Cao, Abhijit Majumder

Bibliographic record

VenuePhysical review. C · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicHigh-Energy Particle Collisions Research
Canadian institutionsUniversity of Regina
FundersNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of ChinaNational Energy Research Scientific Computing CenterU.S. Department of EnergyOffice of ScienceNational Science Foundation
KeywordsPhysicsJet quenchingParticle physicsPartonFlavorQuarkJet (fluid)Quark–gluon plasmaGluonQuantum chromodynamicsMomentum (technical analysis)Nuclear physicsThermodynamics

Abstract

fetched live from OpenAlex

The widespread notion that jets quenched in a quark-gluon-plasma (QGP) are similar in their parton flavor composition to jets in vacuum is critically examined. We demonstrate that while the soft to semihard (low to intermediate transverse momentum ${p}_{T}$) sector of vacuum jets are predominantly bosonic i.e., composed of gluons, sufficiently quenched jets can have an intermediate momentum sector that is predominantly fermionic, dominated by quarks and antiquarks. We demonstrate, using leading-order perturbative QCD processes, that the rate of flavor conversion from a gluon traversing the QGP as part of a jet, to a quark or antiquark, versus the reverse process, grows steadily with falling ${p}_{T}$. Simple diagrammatic estimates are followed by a variety of realistic simulations in static media, which demonstrate qualitatively similar yet quantitatively different fermion enhancements. The relation of this increase in flavor to the observed baryon enhancement at intermediate ${p}_{T}$ is studied in a fully realistic simulation.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.615
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.003

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 head0.032
GPT teacher head0.379
Teacher spread0.348 · 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 teacher head, not a consensus.

Study designTheoretical or conceptual
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

Citations6
Published2023
Admission routes2
Has abstractyes

Explore more

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