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Record W2807779279 · doi:10.1103/physrevc.98.055201

Charge-dependent flow induced by magnetic and electric fields in heavy ion collisions

2018· article· en· W2807779279 on OpenAlexfundno aff
Umut Gürsoy, Dmitri E. Kharzeev, Eric Marcus, Krishna Rajagopal, Chun Shen

Bibliographic record

VenuePhysical review. C · 2018
Typearticle
Languageen
FieldPhysics and Astronomy
TopicHigh-Energy Particle Collisions Research
Canadian institutionsnot available
FundersNuclear PhysicsNatural Sciences and Engineering Research Council of CanadaTürkiye Bilimsel ve Teknolojik Araştırma KurumuCompute CanadaNederlandse Organisatie voor Wetenschappelijk OnderzoekCERNMinisterie van Onderwijs, Cultuur en WetenschapMcGill UniversityU.S. Department of Energy
KeywordsPhysicsRapidityElectric fieldCoulombCharge (physics)Magnetic fieldElectric chargePlasmaLorentz forceFaraday cageAtomic physicsElectric potentialFlow (mathematics)Nuclear physicsVoltageElectronParticle physicsMechanicsQuantum mechanics

Abstract

fetched live from OpenAlex

We investigate the charge-dependent flow induced by magnetic and electric fields in heavy-ion collisions. We simulate the evolution of the expanding cooling droplet of strongly coupled plasma hydrodynamically, using the iEBE-VISHNU framework, and add the magnetic and electric fields as well as the electric currents they generate in a perturbative fashion. We confirm the previously reported effect of the electromagnetically induced currents [Gursoy et al., Phys. Rev. C 89, 054905 (2014)], that is a charge-odd directed flow $\mathrm{\ensuremath{\Delta}}{v}_{1}$ that is odd in rapidity, noting that it is induced by magnetic fields (\`a la Faraday and Lorentz) and by electric fields (the Coulomb field from the charged spectators). In addition, we find a charge-odd $\mathrm{\ensuremath{\Delta}}{v}_{3}$ that is also odd in rapidity and that has a similar physical origin. We furthermore show that the electric field produced by the net charge density of the plasma drives rapidity-even charge-dependent contributions to the radial flow $\ensuremath{\langle}{p}_{T}\ensuremath{\rangle}$ and the elliptic flow $\mathrm{\ensuremath{\Delta}}{v}_{2}$. Although their magnitudes are comparable to the charge-odd $\mathrm{\ensuremath{\Delta}}{v}_{1}$ and $\mathrm{\ensuremath{\Delta}}{v}_{3}$, they have a different physical origin, namely the Coulomb forces within the plasma.

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)0.001
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 score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.017
GPT teacher head0.331
Teacher spread0.315 · 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

Citations128
Published2018
Admission routes1
Has abstractyes

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