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Record W2563882055 · doi:10.1063/1.4973226

A model of anomalous absorption of laser light on ion acoustic turbulence

2017· article· en· W2563882055 on OpenAlexafffund
V. Yu. Bychenkov, W. Rozmus

Bibliographic record

VenuePhysics of Plasmas · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicLaser-Plasma Interactions and Diagnostics
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaRussian Foundation for Basic Research
KeywordsPhysicsCollisionalityPlasmaAtomic physicsAbsorption (acoustics)InstabilityIon acoustic waveAnisotropyIonTwo-stream instabilityTurbulenceElectronComputational physicsOpticsTokamakMechanicsQuantum mechanics

Abstract

fetched live from OpenAlex

Instability of ion acoustic waves due to the return current driven by an electron heat flux is identified as a source of the ion-acoustic turbulence (IAT) in hot, inertial confinement fusion plasmas. Two mechanisms of anomalous absorption are studied, first due to enhanced electron collisionality on ion-acoustic fluctuations and second due to electromagnetic wave conversion into Langmuir waves at the critical density, which is enabled by IAT. An effective absorption coefficient is derived combining the two mechanisms with a stationary IAT spectrum derived from the theory of weak plasma turbulence. Estimates of the return current instability threshold and anomalous absorption are presented for hohlraum plasma in indirect drive fusion experiments. Anomalous absorption is anisotropic due to the angular anisotropy of the IAT spectrum and, according to our theory, can be remarkably effective near the critical density in high Z plasmas. Possible experiments which could identify IAT, and anomalous absorption mechanisms are discussed.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0010.002
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.021
GPT teacher head0.259
Teacher spread0.239 · 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

Citations7
Published2017
Admission routes2
Has abstractyes

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