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Record W2563967268 · doi:10.1103/physreva.92.032712

Independent-electron analysis of the x-ray spectra from single-electron capture in<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msup><mml:mrow><mml:mi>Ne</mml:mi></mml:mrow><mml:mrow><mml:mn>10</mml:mn><mml:mo>+</mml:mo></mml:mrow></mml:msup></mml:math>collisions with He, Ne, and Ar atoms

2015· article· lv· W2563967268 on OpenAlexafffund
Anthony C. K. Leung, Tom Kirchner

Bibliographic record

VenuePhysical Review A · 2015
Typearticle
Languagelv
FieldPhysics and Astronomy
TopicAtomic and Molecular Physics
Canadian institutionsYork University
FundersNatural Sciences and Engineering Research Council of CanadaYork University
KeywordsPhysicsElectron captureElectronSpectral lineAtomic physicsMonte Carlo methodNuclear physicsStatisticsQuantum mechanics

Abstract

fetched live from OpenAlex

We present a theoretical study on the x-ray spectra from single-electron capture in 4.54 keV/amu ${\mathrm{Ne}}^{10+}$-He, -Ne, and -Ar collisions. Single-particle capture probabilities were calculated using the two-center basis generator method within the independent electron model. In this framework we investigated the effects of a time-dependent screening potential that models target response on capture cross sections and x-ray spectra. Excellent agreement is shown with the previously measured relative cross sections and x-ray spectra and calculations based on the classical trajectory Monte Carlo method using the no-response single-particle electron capture probabilities in a multinomial single-electron capture analysis. Our results demonstrate the importance of using this consistent statistical analysis of single-electron capture within the independent electron model; a requirement that a previous calculation for the same collision problem using the two-center atomic-orbital close-coupling method may not have considered.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.942
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0000.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0260.001

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 head0.015
GPT teacher head0.244
Teacher spread0.229 · 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.

Study designTheoretical or conceptual
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

Citations8
Published2015
Admission routes2
Has abstractyes

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