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Record W4200259172 · doi:10.1364/josab.443339

Gravitational acceleration estimation with a nonlinear Mach–Zehnder interferometer

2021· article· en· W4200259172 on OpenAlexaff
Leili Esmaeilifar, Timothy C. Ralph

Bibliographic record

VenueJournal of the Optical Society of America B · 2021
Typearticle
Languageen
FieldComputer Science
TopicQuantum Information and Cryptography
Canadian institutionsUniversity of Calgary
FundersAustralian Research Council
KeywordsInterferometryMach–Zehnder interferometerPhysicsAccelerationNonlinear systemOpticsInterferometric visibilityGravitational accelerationAtom interferometerLimit (mathematics)Astronomical interferometerGravitationClassical mechanicsMathematical analysisQuantum mechanicsMathematics

Abstract

fetched live from OpenAlex

We investigate the use of a nonlinear optical interferometer to estimate local gravitational acceleration. We study a Mach–Zehnder interferometer (MZI) in an optical fountain configuration where a Kerr nonlinear medium is placed in the arms of the interferometer. This configuration allows the parameter estimation to exceed the standard Heisenberg limit of a linear interferometer. Compared to previous studies, we argue that this interferometer configuration is better posed and achieves improved precision for similar scale and pump powers.

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)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.001
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 head0.011
GPT teacher head0.245
Teacher spread0.234 · 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

Citations4
Published2021
Admission routes1
Has abstractyes

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