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Record W2563560569 · doi:10.1103/physreva.93.012502

Time-dependent density-functional-theory studies of collisions involving He atoms: Extension of an adiabatic correlation-integral model

2016· article· en· W2563560569 on OpenAlexafffund
Matthew Baxter, Tom Kirchner

Bibliographic record

VenuePhysical review. A/Physical review, A · 2016
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAtomic and Molecular Physics
Canadian institutionsYork University
FundersNatural Sciences and Engineering Research Council of CanadaYork UniversityCompute Canada
KeywordsTime-dependent density functional theoryAntiprotonPhysicsIonizationAdiabatic processProjectileAtomic physicsHeliumElectronStopping powerDensity functional theoryProtonNuclear physicsQuantum mechanicsIonExcited state

Abstract

fetched live from OpenAlex

A recent model to describe electron correlations in time-dependent density-functional-theory (TDDFT) studies of antiproton-helium collisions is extended to deal with positively charged projectiles. The main complication is that a positively charged projectile can capture electrons in addition to ionizing them to the continuum. As a consequence, within the TDDFT framework one needs to consider three, instead of just one, correlation integrals (${I}_{c}$'s) when formally expressing the probabilities for the one- and two-electron processes in terms of the density. We discuss an extension of an adiabatic model for ${I}_{c}$ to a two-centered system. Total cross sections for single ionization, double ionization, single capture, transfer ionization, and double capture are presented for both proton-helium and ${\text{He}}^{2+}$-He collisions for impact energies in the approximate range 10--1000 keV/amu. One- and two-electron removal cross sections are also presented for the $p$-He system, with a comparison to updated antiproton-helium results. Our results, while mixed, demonstrate the relative importance of dynamic and functional correlations in a TDDFT description of collision processes.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.249
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
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 head0.024
GPT teacher head0.342
Teacher spread0.318 · 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

Citations30
Published2016
Admission routes2
Has abstractyes

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