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Record W4404104472 · doi:10.1002/lpor.202400937

Detection of Nonlinearity in Photonic Lattices

2024· article· en· W4404104472 on OpenAlexaff
Pengbo Jia, Zhaochen Li, Shiqiang Xia, Domenico Bongiovanni, Liqin Tang, Wen-Rong Qi, Yingying Zhang, Xing-Dong Zhao, Keyu Su, Zunlue Zhu, Yi Hu

Bibliographic record

VenueLaser & Photonics Review · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNonlinear Photonic Systems
Canadian institutionsInstitut National de la Recherche Scientifique
FundersChina Postdoctoral Science FoundationNational Natural Science Foundation of China
KeywordsNonlinear systemPhotonicsPhotonic crystalAiry beamAiry functionPhysicsNonlinear opticsNonlinear photonic crystalBeam (structure)OpticsQuantum mechanics

Abstract

fetched live from OpenAlex

Abstract Although periodic photonic structures, especially associated with nonlinearity, play a prominent role in optics nowadays, effective detection of their nonlinearity still remains a critical challenge. Here, an approach is proposed to detect the nonlinearity of photonic lattices in a direct way. By properly launching structured beams, namely Airy beams, into the lattices, the nonlinear response function of the discrete system can be directly obtained in the nonlinearly‐shaped beam profiles. To be specific, a single Airy beam is utilized to map self‐defocusing nonlinearity, while self‐focusing nonlinearity, which is hard to visualize in the bulk case, is readily discerned by employing double Airy beams in photonic structures. The proposed method is validated numerically and experimentally by detecting different types of nonlinearities of photonic lattices fabricated in a nonlinear crystal. These findings introduce a promising route for characterizing the nonlinear response of optical structures, thereby broadening the scope of nonlinear measurement and is expected to be extended into other periodic photonic structures.

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)0.000
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
Metaresearch0.0000.000
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 science0.0000.001
Research integrity0.0000.001
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 head0.015
GPT teacher head0.296
Teacher spread0.281 · 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
Published2024
Admission routes1
Has abstractyes

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