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Record W2079884317 · doi:10.1139/cjp-2014-0449

Effect of nonextensive electrons on dust–ion acoustic wave self-modulation

2015· article· en· W2079884317 on OpenAlexvenueno aff
Naser Panahi, H. Alinejad, Mohammad Mahdavi

Bibliographic record

VenueCanadian Journal of Physics · 2015
Typearticle
Languageen
FieldPhysics and Astronomy
TopicDust and Plasma Wave Phenomena
Canadian institutionsnot available
FundersIslamic Azad University
KeywordsPhysicsInstabilityAdiabatic processParameter spaceElectronIonPlasmaDusty plasmaAstrophysical plasmaNonlinear systemWave packetAmplitudeAtomic physicsQuantum electrodynamicsQuantum mechanics

Abstract

fetched live from OpenAlex

Nonlinear self-modulation of dust–ion acoustic (DIA) waves is studied in an unmagnetized dusty plasma comprising warm adiabatic ions, arbitrarily charged dust particles, and hot nonextensive q-distributed electrons. By employing the multiple space and time scales perturbation, a nonlinear Schrödinger equation is derived for the evolution of the wave amplitude. The existence along with the stability of wave packets are discussed in the parameter space of two oppositely charged dust and ion temperature over different ranges of the nonextensive parameter q. The growth rate of the modulation instability is also given for different values of the q parameter. It is found that the critical wave number at which the instability sets in increases as the nonextensive parameter q increases. This leads to a wider range (in spatial extension) of the stable envelope solitons. It is also found that the effects of ion temperature and negative (positive) dust concentration significantly modify the criteria for the modulation instability of DIA waves. Our finding should elucidate the nonlinear electrostatic structures that propagate in astrophysical and cosmological plasma scenarios where nonextensive particles exist: such as instellar plasma, stellar polytropes, cosmic radiation, and systems with long-rang interaction.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.554
Threshold uncertainty score0.500

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 head0.012
GPT teacher head0.220
Teacher spread0.208 · 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 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

Citations3
Published2015
Admission routes1
Has abstractyes

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