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Record W2800414611 · doi:10.1088/1361-6455/aac34e

RBED cross sections for the ionization of atomic inner shells by electron-impact

2018· article· en· W2800414611 on OpenAlexafffund
Xiaoya Judy Wang, Jan Seuntjens, José M. Fernández‐Varea

Bibliographic record

VenueJournal of Physics B Atomic Molecular and Optical Physics · 2018
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAtomic and Molecular Physics
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaMinisterio de Economía y Competitividad
KeywordsAtomic physicsIonizationElectron ionizationElectronMaterials scienceIonPhysicsNuclear physics

Abstract

fetched live from OpenAlex

Abstract The relativistic binary-encounter-dipole (RBED) model for electron-impact ionization of atoms combines classical binary-encounter theory and the asymptotic dipole interaction, which is based on the plane-wave Born approximation, with the only non-trivial ingredient being the optical oscillator strength (OOS). Due to the difficulty of obtaining accurate OOSs, the performance of the RBED model has so far not been fully assessed. In the present work we compare RBED inner-shell ionization cross sections (total and differential) of neutral atoms evaluated using three types of OOSs, namely an empirical power-law OOS, analytical hydrogenic OOSs and ab initio OOSs calculated numerically from self-consistent atomic potentials. We find that, compared to the distorted-wave Born approximation (DWBA), the RBED with either hydrogenic or numerical OOSs generally yields more accurate total cross sections (TCSs) than the RBED with the power-law OOS, especially for the most tightly bound shells. In the highly relativistic limit the RBED model does not recover the Bethe asymptotic behavior because of its different energy-dependent prefactor, hence we investigate an alternative prefactor which restores the correct Bethe asymptote. Finally, we suggest multiplying the RBED differential cross sections (DCSs) by the ratio of DWBA to RBED TCSs and verify that this renormalization improves the agreement with the DWBA DCSs.

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: none
Teacher disagreement score0.541
Threshold uncertainty score0.867

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
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 head0.006
GPT teacher head0.279
Teacher spread0.273 · 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

Citations2
Published2018
Admission routes2
Has abstractyes

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