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Record W4414520806 · doi:10.34133/research.0941

Diffusion Wake: A Distinctive Consequence of the Mach-Cone Wake Induced by Supersonic Jets in High-Energy Heavy-Ion Collisions

2025· article· en· W4414520806 on OpenAlexaff
Z. C. Yang, Xin-Nian Wang

Bibliographic record

VenueResearch · 2025
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Turbulent Flows
Canadian institutionsInstitute of Particle Physics
Fundersnot available
KeywordsRapiditySupersonic speedWakeAsymmetryDiffusionRidgeSIGNAL (programming language)

Abstract

fetched live from OpenAlex

In this Research Perspective, we briefly review the diffusion wake, a distinctive consequence of the Mach-cone wake induced by the supersonic jets in ultrarelativistic heavy-ion collisions. The diffusion wake depletes soft hadrons in the direction opposite to the propagating jet. According to coupled transport and hydrodynamic simulations, a valley in the 2-dimensional jet–hadron correlation in azimuthal angle and rapidity arises on the top of the multiple-parton interaction ridge as an unambiguous signal of the diffusion wake induced by γ -jets in heavy-ion collisions. In dijet events with a finite rapidity gap, the rapidity asymmetry of the jet–hadron correlation has been shown to be a robust signal of the diffusion wake. The same rapidity asymmetry can also be applied to γ -jet events, and both are background-free. Experimental measurements of these signals can provide valuable insights into the properties of the quark–gluon plasma formed in high-energy heavy-ion collisions.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.019
GPT teacher head0.278
Teacher spread0.259 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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