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Record W1973998397 · doi:10.1103/physrevc.78.044003

Mott-Schwinger scattering of polarized low energy neutrons up to thermal energies

2008· article· en· W1973998397 on OpenAlexaff
Michael Gericke, J. D. Bowman, Mikkel B. Johnson

Bibliographic record

VenuePhysical Review C · 2008
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAtomic and Subatomic Physics Research
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsPhysicsNeutronScatteringNeutron temperatureNuclear physicsNeutron scatteringPolarization (electrochemistry)HadronAtomic physicsQuantum mechanics

Abstract

fetched live from OpenAlex

The availability of new, high-intensity, cold and thermal neutron sources has opened the possibility of performing high-precision fundamental neutron physics experiments, including measurements that study the hadronic weak interaction and standard model test measurements, using neutron $\ensuremath{\beta}$ decay. The observables in these experiments are usually correlated with the direction of neutron polarization and are often very small $({10}^{\ensuremath{-}8}\ensuremath{\rightarrow}{10}^{\ensuremath{-}6})$. Mott-Schwinger scattering of polarized neutrons can produce spin-dependent shifts in beam centroids, which has the potential to produce significant systematic effects for these types of experiments. An accurate calculation of this process for neutral atoms and basic molecules has not been carried out for low neutron energies. In this work, we derive a general expression for the electromagnetic (Mott-Schwinger) contributions to the analyzing power for low-energy neutron scattering. We obtain numerical results for 11 nuclei in the range of $A=1$ to $A=208$ and provide a series of graphs for easy reference and interpolation between $A$ values. We also estimate the contribution of spin-dependent nucleon-nucleon forces and apply our results to determine the analyzing power of parahydrogen. Numerical calculations are performed to determine the analyzing power for the parahydrogen molecule and are compared to results obtained using analytical expressions.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
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 head0.023
GPT teacher head0.306
Teacher spread0.284 · 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 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

Citations14
Published2008
Admission routes1
Has abstractyes

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