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Record W2611659452 · doi:10.1117/12.2262211

An auto-locked laser system for precision metrology and lidar applications

2017· article· en· W2611659452 on OpenAlexaff
H. C. Beica, A. Carew, A. Vorozcovs, P. Dowling, Alexander Pouliot, Boris Barron, A. Kumarakrishnan

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2017
Typearticle
Languageen
FieldChemistry
TopicSpectroscopy and Laser Applications
Canadian institutionsYork University
Fundersnot available
KeywordsLaserLaser linewidthDistributed feedback laserOpticsTunable laserMaterials scienceFiber Bragg gratingAllan varianceFiber laserLaser power scalingOptoelectronicsPhysicsStandard deviation

Abstract

fetched live from OpenAlex

We present a unique external cavity diode laser system that can be auto-locked with reference to atomic and molecular spectra. The vacuum-sealed laser head design uses an interchangeable base-plate comprised of a laser diode and optical elements that can be selected for desired wavelength ranges. The feedback light to the laser diode is provided by a narrow-band interference filter, which can be tuned from outside the laser cavity to fineadjust the output wavelength in vacuum. To stabilize the laser frequency, the digital laser controller relies either on a pattern-matching algorithm stored in memory, or on first or third derivative feedback. We have used the laser systems to perform spectroscopic studies in rubidium at 780 nm, and in iodine at 633 nm. The linewidth of the 780-nm laser system was measured to be ∼500 kHz, and we present Allan deviation measurements of the beat note and the lock stability. Furthermore, we show that the laser system can be the basis for a new class of lidar transmitters in which a temperature-stabilized fiber-Bragg grating is used to generate frequency references for on-line points of the transmitter. We show that the fiber-Bragg grating spectra can be calibrated with reference to atomic transitions.

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 score0.352
Threshold uncertainty score0.946

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 science0.0010.000
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 head0.010
GPT teacher head0.258
Teacher spread0.248 · 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

Citations5
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIESame topicSpectroscopy and Laser ApplicationsFrench-language works237,207