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Event shape engineering via Glauber MC model

2024· article· en· W4392135525 on OpenAlexaff
Hokong Peng, Luning Shi, Yuan Cao, Yubang Huang, Junheng Huang

Bibliographic record

VenueApplied and Computational Engineering · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicHigh-Energy Particle Collisions Research
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsGlauberEvent (particle physics)AnisotropyPlot (graphics)Yield (engineering)PhysicsParticle physicsPlasmaStatistical physicsGeometryMathematicsNuclear physicsStatisticsOpticsAstrophysicsThermodynamics

Abstract

fetched live from OpenAlex

In our exploration of event shape engineering, the Glauber model served as a foundational tool for under- standing the anisotropic geometry of the Quark-Gluon Plasma (QGP). Utilizing the TGlauberMC-3.2 model within ROOT, we systematically analyzed one million events. From the ϵ2 & Npart plot, our data revealed an average maximum dN value of 12.00 with associated parameters: ϵ2 = 0.91, ψ2 =2.74, ψ3 =0.91 and Npart = 14.00. These findings illuminate the distinct configurations that yield the most pronounced anisotropic geometries of the QGP, providing insights into optimizing event shape configurations.

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)0.003
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 score0.019
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0030.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.006
GPT teacher head0.231
Teacher spread0.225 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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