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Cascade weak-value amplification for optic-fiber-based Sagnac interferometers

2023· preprint· en· W4321615295 on OpenAlexafffund
Huang Jing-Hui, Xiangyun Hu, Duan Xue-Ying, Wang Guang-Gun

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicGeophysics and Sensor Technology
Canadian institutionsUniversity of Ottawa
FundersChina Scholarship CouncilNational Natural Science Foundation of ChinaUniversity of OttawaNational Science Foundation
KeywordsPhysicsCascadeInterferometryOpticsSagnac effectVernier scaleAstronomical interferometerEnvelope (radar)Rotation (mathematics)SIGNAL (programming language)Noise (video)Telecommunications

Abstract

fetched live from OpenAlex

In this paper, we propose our advantageous research leading to a new scheme for angular rotation $\Omega$ measurement in an optic-fiber-based Sagnac interferometer based on cascade weak-value amplification (CWVA). CWVA is a modified standard weak-value amplification (SWVA) technique for further enhancing the temporal shifts based on the principle of the Vernier effect. By choosing the appropriate CWVA parameters and the repetition time intervals of the Vernier scale, the temporal shifts in SWVA can be further amplified by measuring the envelope shifts in CWVA. Our simulation results indicated that CWVA can demonstrate the detection of tiny rotations at the range of 1.0 $\times$ $10^{-9}$ rad/s $\leq$ $\Omega$ $\leq$ 10 $\times$ $10^{-9}$ rad/s with higher sensitivity and larger signal-to-noise ratios than SWVA. The enhancement with a larger detection band may have a high influence on physics and related sciences, like rotational seismology and gravitational sensing.

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: none
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.037
GPT teacher head0.256
Teacher spread0.219 · 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".

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Citations0
Published2023
Admission routes2
Has abstractyes

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