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Record W2242454189 · doi:10.1139/p07-002

Many-body calculation of helium <sup>1</sup>D–<sup>3</sup>D term intervals for 1<i>snd</i> (<i>n</i> = 12 ∼ 20) high Rydberg states

2006· article· en· W2242454189 on OpenAlexvenueno aff
Li-Ming He, Wei Cao

Bibliographic record

VenueCanadian Journal of Physics · 2006
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Chemical Physics Studies
Canadian institutionsnot available
FundersEast China University of Science and TechnologyNational Natural Science Foundation of ChinaSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungNational Science Foundation
KeywordsPhysicsRydberg formulaWave functionHeliumBound statePerturbation theory (quantum mechanics)Series expansionAtomic physicsSchrödinger equationHelium-4Three-body problemQuantum mechanicsMathematical physicsIonization

Abstract

fetched live from OpenAlex

With many-body perturbation theory, 1D–3D term intervals of helium 1snd (n = 12 ∼ 20) configurations are calculated. Based on two different models, Rayleigh-Schrodinger perturbation expansion terms consisting of bound states only, and those of continua are evaluated, respectively. As for bound states, zeroth-order wave functions are strictly generated from self-iteration solutions of the Hartree equation and residues of infinite expansion series are dealt with by the integral processing method, while a simplified hydrogen potential is adopted to get the continua. Using Rayleigh–Schrodinger expansions, we evaluate exchange energy up to third-order terms. It is found that level splittings are mainly attributed to summations over bound states. The fine-structure level splittings yielded here are found to agree quite well with experimental results.PACS Nos.: 31.15.Md, 32.10Fn, 02.60Ed

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)0.001
metaresearch head score (Gemma)0.001
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
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 head0.008
GPT teacher head0.228
Teacher spread0.219 · 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

Citations0
Published2006
Admission routes1
Has abstractyes

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