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Record W4392346930 · doi:10.31349/revmexfis.70.021201

A thermo-magnetic bag model for the quark-gluon plasma

2024· article· en· W4392346930 on OpenAlexfundno aff
Paulina Fernanda Valenzuela-Coronado, Maria Elena Tejeda Yeomans, J. Torres-Arenas

Bibliographic record

VenueRevista Mexicana de Física · 2024
Typearticle
Languageen
FieldEngineering
TopicSuperconducting Materials and Applications
Canadian institutionsnot available
FundersInstitut Périmètre de physique théoriqueUniversidad de Guanajuato
KeywordsPhysicsQuark–gluon plasmaPlasmaParticle physicsGluonNuclear physicsQuarkQuantum electrodynamics

Abstract

fetched live from OpenAlex

In this work we study the pressure of the quark-gluon plasma (QGP) in the presence of a weak magnetic field, using a minimally enhanced model of a weakly interacting gas of quasi-particles in a thermal bath. We include the magnetic field effects through the quark mass that has been modified using a recently proposed thermo-magnetic coupling. This thermo-magnetic coupling emerges form the quark-gluon vertex in the HTL approximation [1]. We use Lattice QCD [2] data, to constrain the thermo-magnetic bag function of the quasi-particle model and provide an estimate of the thermo-magnetic vacuum energy density. We then compute the transverse pressure of the system and compare with similar results from the literature. We find that the inverse magnetic catalysis already built within this thermo-magnetic coupling allows a robust description of this Lattice QCD data for the pressure of the QGP in the presence of a weak magnetic field. The extension to the thermal quasi-particle model we have introduced here, makes it easier to pursue further phenomenological studies that require simulations with an EoS that has integrable quasi-particle thermodynamic variables which have the general features of lattice data in the weak magnetic field regime.

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 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 score0.618
Threshold uncertainty score0.352

Codex and Gemma teacher scores by category

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

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 head0.023
GPT teacher head0.248
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 teacher head, 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
Published2024
Admission routes1
Has abstractyes

Explore more

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