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Record W3158599703 · doi:10.1002/lpor.202000576

Rotational Doppler Frequency Shift from Time‐Evolving High‐Order Pancharatnam–Berry Phase: A Metasurface Approach

2021· article· en· W3158599703 on OpenAlexaff
Fuyong Yue, A. Aadhi, Riccardo Piccoli, Vincenzo Aglieri, Roberto Macaluso, Andréa Toma, Roberto Morandotti, Luca Razzari

Bibliographic record

VenueLaser & Photonics Review · 2021
Typearticle
Languageen
FieldPhysics and Astronomy
TopicOrbital Angular Momentum in Optics
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsAngular momentumPhysicsGeometric phaseDoppler effectAdiabatic processOpticsWavelengthHamiltonian (control theory)Classical mechanicsQuantum mechanics

Abstract

fetched live from OpenAlex

Abstract The Doppler frequency shift of sound or electromagnetic waves has been widely investigated in many different contexts and, nowadays, represents a formidable tool in medicine, engineering, astrophysics, and optics. Such effect is commonly described in the framework of the universal energy‐momentum conservation law. In particular, the rotational Doppler effect has been recently demonstrated using light carrying orbital angular momentum. When a wave undergoes a cyclic adiabatic transformation of its Hamiltonian, it is known to acquire the so‐called Pancharatnam–Berry (PB) phase. In this work, an experimental evidence of the direct connection between the high‐order PB phase time evolution on the Poincaré sphere and the rotational Doppler frequency shift of light is provided. A metasurface operating at telecom wavelengths is employed to impose a total (spin and orbital) angular momentum (TAM) on the light wave, while two TAM converters ensure a closed cycle on the Poincaré sphere. By rotating one of the converters, a significant Doppler frequency shift is observed without variation of the output TAM. The proposed metasurface‐based approach offers new advanced ways to engineer the frequency content of light.

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), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.557
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0220.001

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 head0.012
GPT teacher head0.260
Teacher spread0.249 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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
Published2021
Admission routes1
Has abstractyes

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