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Record W2892856921 · doi:10.1016/j.asr.2018.09.029

Perturbation theory based solution of the pitch-angle dependent cosmic ray diffusion equation

2018· article· en· W2892856921 on OpenAlexafffund
A. Shalchi, Michael Gammon

Bibliographic record

VenueAdvances in Space Research · 2018
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSolar and Space Plasma Dynamics
Canadian institutionsUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsFokker–Planck equationPhysicsPitch angleCosmic rayEigenvalues and eigenvectorsEigenfunctionPerturbation theory (quantum mechanics)Diffusion equationConvection–diffusion equationCOSMIC cancer databasePerturbation (astronomy)Classical mechanicsStatistical physicsDifferential equationAstrophysicsMechanicsQuantum mechanics

Abstract

fetched live from OpenAlex

The motion of cosmic rays and energetic particles in general is described via transport equations. If a pitch-angle dependent description is desired, a Fokker-Planck equation provides the basis for exploring the particle motion. To date, no exact and pure analytical solution to the two-dimensional cosmic ray Fokker-Planck equation has been found. Previous attempts are either solutions of the pitch-angle averaged equation or the space integrated equation. Of course, numerical calculations can easily be performed but those are not very useful in astrophysical applications. In the current paper we employ perturbation theory in order to solve the cosmic ray Fokker-Planck equation. Corrections up to fourth-order to the eigenvalues and second-order eigenfunctions are computed. Our results are compared with previous solutions. Furthermore, we discuss applications such as estimating higher order correlations occurring in analytical treatments of perpendicular transport.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
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 head0.025
GPT teacher head0.333
Teacher spread0.308 · 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

Citations3
Published2018
Admission routes2
Has abstractyes

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