MétaCan
Menu
Back to cohort
Record W4285042027 · doi:10.1364/oe.460524

Optoelectronic oscillator with improved sidemode suppression by joint use of spectral Vernier effect and parity-time symmetry

2022· article· en· W4285042027 on OpenAlexaff
Minghai Li, Lingzhi Li, Ruidong Cao, Jiejun Zhang, Jianping Yao

Bibliographic record

VenueOptics Express · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Fiber Laser Technologies
Canadian institutionsUniversity of Ottawa
FundersNational Key Research and Development Program of ChinaNational Natural Science Foundation of China
KeywordsPhase noiseVernier scaleOpticsMaterials scienceFree spectral rangedBcOscillation (cell signaling)PhysicsOptoelectronicsResonator

Abstract

fetched live from OpenAlex

An optoelectronic oscillator (OEO) with improved sideband suppression by joint use of the spectral Vernier effect and parity-time (PT) symmetry is proposed and experimentally demonstrated. The spectral Vernier effect is implemented using two mutually coupled loops with different loop lengths, to increase the effective free spectral range (FSR). To further increase the mode selection capability to ensure stable single-frequency oscillation with an increased sidemode suppression ratio (SMSR), PT symmetry is implemented, in which the two mutually coupled loops are controlled with balanced gain and loss. Thanks to the combined effects, stable single-mode oscillation with a significantly increased SMSR is achieved. The proposed OEO is studied theoretically and evaluated experimentally. The results show that for a generated microwave signal at 10 GHz, the SMSR is 67.68 dB, which is increased by 11.20 dB or 26.05 dB, when using only the spectral Vernier effect or only the PT symmetry. Thanks to the long length of the longer loop, good phase noise performance is still maintained. The measurement shows that a phase noise as low as -124.5 dBc/Hz at an offset frequency of 10 kHz is achieved.

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 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 score0.012
Threshold uncertainty score0.768

Codex and Gemma teacher scores by category

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 science0.0000.000
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 head0.005
GPT teacher head0.209
Teacher spread0.203 · 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.

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

Citations13
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueOptics ExpressSame topicAdvanced Fiber Laser TechnologiesFrench-language works237,207