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Record W4301373932 · doi:10.1093/mnras/stac2807

The impact of resistive electric fields on particle acceleration in reconnection layers

2022· article· en· W4301373932 on OpenAlexfundno aff
Eleonora Puzzoni, A. Mignone, G. Bodo

Bibliographic record

VenueMonthly Notices of the Royal Astronomical Society · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsnot available
FundersInstitut sur la Nutrition et les Aliments Fonctionnels
KeywordsPhysicsParticle accelerationElectric fieldMagnetohydrodynamic driveCurrent sheetResistive touchscreenMagnetic reconnectionContext (archaeology)Computational physicsMechanicsAccelerationMagnetic fieldMagnetohydrodynamicsClassical mechanicsElectrical engineeringQuantum mechanics

Abstract

fetched live from OpenAlex

ABSTRACT In the context of particle acceleration in high-energy astrophysical environments featuring magnetic reconnection, the importance of the resistive term of the electric field compared to the convective one is still under debate. In this work, we present a quantitative analysis through 2D magnetohydrodynamic numerical simulations of tearing-unstable current sheets coupled to a test-particles approach, performed with the pluto code. We find that the resistive field plays a significant role in the early-stage energization of high-energy particles. Indeed, these particles are first accelerated due to the resistive electric field when they cross an X-point, created during the fragmentation of the current sheet. If this preliminary particle acceleration mechanism dominated by the resistive field is neglected, particles cannot reach the same high energies. Our results support therefore the conclusion that the resistive field is not only non-negligible but it does actually play an important role in the particle acceleration mechanism.

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)0.002
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 score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
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)0.0030.000

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 head0.007
GPT teacher head0.220
Teacher spread0.213 · 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

Citations4
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueMonthly Notices of the Royal Astronomical Society→Same topicIonosphere and magnetosphere dynamics→French-language works237,207→