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Channeling of Relativistic Laser Pulses, Surface Waves, and Electron Acceleration

2012· article· en· W2321069165 on OpenAlexaff
Neda Naseri, D. Pesme, W. Rozmus, Konstantin Popov

Bibliographic record

VenuePhysical Review Letters · 2012
Typearticle
Languageen
FieldPhysics and Astronomy
TopicLaser-Plasma Interactions and Diagnostics
Canadian institutionsUniversity of OttawaUniversity of Alberta
Fundersnot available
KeywordsPhysicsBetatronAtomic physicsElectronLaserPlasma channelAccelerationPulse (music)ExcitationPlasmaPicosecondParticle accelerationOpticsNuclear physicsClassical mechanicsQuantum mechanics

Abstract

fetched live from OpenAlex

The interaction of a high-energy relativistic laser pulse with an underdense plasma is studied by means of 3-dimensional particle in cell simulations and theoretical analysis. For powers above the threshold for channeling, the laser pulse propagates as a single mode in an electron-free channel during a time of the order of 1 picosecond. The steep laser front gives rise to the excitation of a surface wave along the sharp boundaries of the ion channel. The surface wave first traps electrons at the channel wall and preaccelerates them to relativistic energies. These particles then have enough energy to be further accelerated in a second stage through an interplay between the acceleration due to the betatron resonance and the acceleration caused by the longitudinal part of the surface wave electric field. It is necessary to introduce this two-stage process to explain the large number of high-energy electrons observed in the simulations.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
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)0.0020.000

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 head0.295
Teacher spread0.278 · 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 designBench or experimental
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

Citations36
Published2012
Admission routes1
Has abstractyes

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