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Record W4323863033 · doi:10.21203/rs.3.rs-2574423/v1

A Random Optical Parametric Oscillator

2023· preprint· en· W4323863033 on OpenAlexaff
Pedro Tovar, Jean Pierre von der Weid, Yuan Wang, Liang Chen, Xiaoyi Bao

Bibliographic record

VenueResearch Square · 2023
Typepreprint
Languageen
FieldPhysics and Astronomy
TopicRandom lasers and scattering media
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsOptical parametric oscillatorParametric statisticsParametric oscillatorOptical parametric amplifierPhysicsMathematicsOpticsStatisticsLaserOptical amplifier

Abstract

fetched live from OpenAlex

Abstract Optical parametric oscillators provide short coherent light pulses at widely tunable wavelengths. Their primary drawback is the requirement of precise cavity control, making such devices specially challenging to develop in long cavities, and with oscillation limited to a fixed repetition rate. Herein, exploiting the inherent disorder of the refractive index in single-mode fibres we developed the first random optical parametric oscillator - the parametric analogous of random lasers. Parametric amplification is provided by modulation instability through the Χ (3) non-linearity and feedback is given by Rayleigh scattering. The system is realised in a pulsed configuration, with extremely small (<0.001%) feedback. To enhance the weak feedback, a novel piecewise distributed random cavity is demonstrated. In contrast to conventional parametric oscillators, the emission is sustained at arbitrary rates and dispensing any kind of cavity control loop, resulting in a far more versatile device, representing an important step toward the development of stable and tunable - both in wavelength and repetition rate - parametric oscillators. The ultra-long cavity used in experiments (~5km) enabled the generation of four-wave mixing products between the pump and the singly-resonant oscillator, giving rise to a train of picosecond-pulses, which would find applications in ultra-fast optics.

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)0.002
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 categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.372
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.002

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 head0.089
GPT teacher head0.397
Teacher spread0.308 · 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.

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

Citations0
Published2023
Admission routes1
Has abstractyes

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