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Record W2025584798 · doi:10.1063/1.4754004

Multi-temperature representation of electron velocity distribution functions. I. Fits to numerical results

2012· article· en· W2025584798 on OpenAlexaff
Amir Abolhassani, J. P. Matte

Bibliographic record

VenuePhysics of Plasmas · 2012
Typearticle
Languageen
FieldPhysics and Astronomy
TopicDust and Plasma Wave Phenomena
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsPhysicsLaguerre polynomialsElectronDistribution functionPlasmaVelocity MomentsRepresentation (politics)Distribution (mathematics)Kinetic energyRelaxation (psychology)Electron temperatureStatistical physicsQuantum mechanicsMathematical analysisMathematics

Abstract

fetched live from OpenAlex

Electron energy distribution functions are expressed as a sum of 6–12 Maxwellians or a sum of 3, but each multiplied by a finite series of generalized Laguerre polynomials. We fitted several distribution functions obtained from the finite difference Fokker-Planck code “FPI” [Matte and Virmont, Phys. Rev. Lett. 49, 1936 (1982)] to these forms, by matching the moments, and showed that they can represent very well the coexistence of hot and cold populations, with a temperature ratio as high as 1000. This was performed for two types of problems: (1) the collisional relaxation of a minority hot component in a uniform plasma and (2) electron heat flow down steep temperature gradients, from a hot to a much colder plasma. We find that the multi-Maxwellian representation is particularly good if we accept complex temperatures and coefficients, and it is always better than the representation with generalized Laguerre polynomials for an equal number of moments. For the electron heat flow problem, the method was modified to also fit the first order anisotropy f1(x,v,t), again with excellent results. We conclude that this multi-Maxwellian representation can provide a viable alternative to the finite difference speed or energy grid in kinetic codes.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.497
Threshold uncertainty score0.577

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 head0.021
GPT teacher head0.272
Teacher spread0.251 · 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

Citations4
Published2012
Admission routes1
Has abstractyes

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