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Record W1675959614 · doi:10.1117/12.2308112

High performances frequency-stabilized semiconductor laser metrology sources for space-borne spectrometers

2017· article· en· W1675959614 on OpenAlexaffabout
C. Latrasse, F. Pelletier, Aidan M. Doyle, Simon Savard, A. Babin, F. Costin, A. Biron, Jean-François Cliche, M. Têtu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Fiber Laser Technologies
Canadian institutionsTeraXion (Canada)
Fundersnot available
KeywordsLaser linewidthLaserMetrologydBcOpticsPhase noiseMaterials scienceInterferometryRelative intensity noiseSemiconductor laser theorySpectrometerCalibrationFiber laserOptoelectronicsPhysics

Abstract

fetched live from OpenAlex

In a project with the Canadian Space Agency (CSA), we have developed prototypes of 1.55 μm frequencystabilized lasers for space applications. These lasers can be used as metrology sources for internal calibration of spectrometers such as the Cross-track Infrared Sounder (CrIS). Our prototypes include a 1552 nm DFB laser frequency-locked to H13CN using external phase modulation. The prototypes feature high quality characteristics such as CW output power of 8 mW and a narrow linewidth of 1.5 MHz. The frequency of the laser is known to a few ppm. The frequency stability levels at 10-10 between 30 and 10 000 s. The relative intensity noise (RIN) falls from -100 to -140 dBc/Hz between 1 Hz and 10 kHz, and levels at -140 dBc/Hz between 10 kHz and 1 MHz. Further improvement to reduce the linewidth to a few kHz can be provided using an all-fiber interferometer and correction of the laser injection current accordingly.

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: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.009

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 studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.016
GPT teacher head0.265
Teacher spread0.250 · 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

Citations1
Published2017
Admission routes2
Has abstractyes

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