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Record W2592049826 · doi:10.1117/12.2250653

An auto-locked diode laser system for precision metrology

2017· article· en· W2592049826 on OpenAlexaff
H. C. Beica, A. Carew, A. Vorozcovs, P. Dowling, Alexander Pouliot, Gurpreet Singh, A. Kumarakrishnan

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Fiber Laser Technologies
Canadian institutionsYork University
Fundersnot available
KeywordsLaser linewidthLaserAllan varianceMetrologyOpticsAccuracy and precisionSemiconductor laser theoryDistributed feedback laserMaterials scienceDiodePhysicsComputer scienceStandard deviationOptoelectronics

Abstract

fetched live from OpenAlex

We describe a diode laser system for precision metrology that relies on adaptations of a well-known design based on optical feedback from an interference filter. The laser head operates with an interchangeable base-plate, which allows for single-mode performance at distinct wavelengths of 633 nm and 780 nm. Frequency drifts are effectively suppressed by using a vacuum-sealed laser head, thereby allowing the laser frequency to be stabilized on time-scales of several hours. Using a digital auto-lock controller, we show that the laser frequency can be stabilized with respect to selected iodine and rubidium spectral lines. The controller can be programmed to use a pattern-matching algorithm or generate first- and third-derivative error signals for peak locking. Beat note characterization has demonstrated a short-term linewidth of ~2 MHz and an Allan deviation of 3.5 × 10-9 for a measurement time τ = 500 s. The laser characteristics have also enabled high-precision gravity measurements with accuracies of a few parts-per-billion (ppb).

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)0.001
metaresearch head score (Gemma)0.001
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: none
Teacher disagreement score0.010
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.004

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 head0.011
GPT teacher head0.252
Teacher spread0.241 · 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

Citations6
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIESame topicAdvanced Fiber Laser TechnologiesFrench-language works237,207