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Record W1985460989 · doi:10.1139/p05-017

Including the strong nuclear force in antihydrogen-scattering calculations

2005· article· en· W1985460989 on OpenAlexvenueno aff
S. Jonsell, Alejandro Sáenz, Piotr Froelich, B. Zygelman, A. Dalgarno

Bibliographic record

VenueCanadian Journal of Physics · 2005
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAtomic and Molecular Physics
Canadian institutionsnot available
FundersStifterverbandVetenskapsrådet
KeywordsPhysicsScatteringAntihydrogenAnnihilationElastic scatteringScattering lengthAtomic physicsPositroniumGround statePolarization (electrochemistry)AntiprotonBound stateNuclear physicsProtonPositronQuantum mechanicsElectronAntimatter

Abstract

fetched live from OpenAlex

We investigate two methods to include the strong nuclear force in hydrogen–antihydrogen scattering calculations. First, we construct a model optical potential with parameters determined by the measured shift and width of the protonium ground state. Although this potential is a very crude model for the strong nuclear force, its parameters may be adjusted to reproduce both bound states and low-energy annihilation cross sections to within the experimental accuracy. It is then shown that this potential may be reduced to a short-distance boundary condition in terms of the proton–antiproton strong-interaction scattering length. Elastic and annihilation cross sections for ground-state hydrogen–antihydrogen scattering are calculated for s- and p-waves, and collision energies up to 1 atomic unit. The two methods are found to agree to within about 1%. The main source of discrepancy is that the scattering-length approach does not account for vacuum polarization, relativistic, and finite-size corrections. We verify that the range of the strong interaction potential does not affect the hydrogen–antihydrogen s-wave scattering properties, and that the strong interaction has negligible influence on p-wave scattering. PACS Nos.: 36.10.-k, 34.90.+q

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.474
Threshold uncertainty score0.333

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.017
GPT teacher head0.242
Teacher spread0.225 · 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 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

Citations14
Published2005
Admission routes1
Has abstractyes

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