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Record W2606233223 · doi:10.1063/1.4996866

Noise refocusing in a five-blade neutron interferometer

2017· article· en· W2606233223 on OpenAlexafffund

Bibliographic record

VenueJournal of Applied Physics · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAtomic and Subatomic Physics Research
Canadian institutionsCanadian Institute for Advanced ResearchPerimeter InstituteUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaCanada Excellence Research Chairs, Government of CanadaCanada First Research Excellence FundNational Science Foundation
KeywordsInterferometryNoise (video)DephasingNeutronAtom interferometerVibrationQuantum decoherenceQuantum noise

Abstract

fetched live from OpenAlex

We provide a quantum information description of a proposed five-blade neutron interferometer geometry and show that it is robust against low-frequency mechanical vibrations and dephasing due to the dynamical phase. The extent to which the dynamical phase affects the contrast in a neutron interferometer is experimentally shown. In our model, we consider the coherent evolution of a neutron wavepacket in an interferometer crystal blade and simulate the effect of mechanical vibrations and momentum spread of the neutron through the interferometer. The standard three-blade neutron interferometer is shown to be immune to dynamical phase noise but prone to noise from mechanical vibrations, and the decoherence free subspace four-blade neutron interferometer is shown to be immune to mechanical vibration noise but prone to noise from the dynamical phase. Here, we propose a five-blade neutron interferometer and show that it is immune to both low-frequency mechanical vibration noise and dynamical phase noise.

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 score0.551
Threshold uncertainty score0.590

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 science0.0010.000
Research integrity0.0000.001
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 head0.027
GPT teacher head0.305
Teacher spread0.279 · 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

Citations6
Published2017
Admission routes2
Has abstractyes

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