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Record W4403213067 · doi:10.1103/physreva.110.043306

Optimization of Bragg scattering from atomic gratings for echo interferometry

2024· article· en· W4403213067 on OpenAlexafffund
Gehrig Carlse, Jaskaran Randhawa, Eduardo Ramos, Thomas Vacheresse, Alexander Pouliot, A. Carew, A. Kumarakrishnan, B. Barrett

Bibliographic record

VenuePhysical review. A/Physical review, A · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAtomic and Subatomic Physics Research
Canadian institutionsUniversity of New BrunswickYork University
FundersArmy Research OfficeNatural Sciences and Engineering Research Council of CanadaCanada Foundation for InnovationYork UniversityOntario Innovation Trust
KeywordsEcho (communications protocol)InterferometryOpticsScatteringBragg's lawMaterials sciencePhysicsDiffractionComputer science

Abstract

fetched live from OpenAlex

We investigate the characteristics of near resonant Bragg scattering, the underlying process that produces a signal in a grating-echo atom interferometer. We explore how the density modulation produced in an atomic sample by momentum state interference and optical channeling can be optimally read out using a coherently Bragg-scattered traveling-wave electric field. When both the channeling and Bragg scattering processes are optimized, we report a 20-fold increase in scattering efficiency. Our results suggest that a quantitative understanding of the atomic confinement achievable by channeling is possible through an analysis of reflectivity spectra.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.653
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
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 head0.021
GPT teacher head0.396
Teacher spread0.374 · 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 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

Citations4
Published2024
Admission routes2
Has abstractyes

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