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Record W4391859595 · doi:10.1364/ofs.2023.w2.5

Improved Closed-Loop Slow-Light Temperature Sensor With Millidegree Resolution for Laser Cooling

2023· article· en· W4391859595 on OpenAlexaff
Enkeleda Balliu, Lauris Talbot, Chun-Wei Chen, Benjamin Frey, Bailey Meehan, Thomas W. Hawkins, John Ballato, Tommy Boilard, Martin Bernier, Michel J. F. Digonnet

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicOptical properties and cooling technologies in crystalline materials
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsLoop (graph theory)Temperature measurementMaterials scienceOpticsTemperature controlClosed loopLaserResolution (logic)Laser coolingOptoelectronicsControl theory (sociology)PhysicsComputer scienceEngineeringMathematicsThermodynamicsControl (management)Artificial intelligenceControl engineering

Abstract

fetched live from OpenAlex

For applications such as laser cooling of doped fibers, where it is critical to measure accurately the temperature of a cooled fiber that is very close to room temperature, it is paramount to develop a reliable, very short (mm) fiber temperature sensor with millikelvin resolution and very little drift. We report a second generation of a unique slow-light fiber Bragg grating (FBG) temperature sensor that meets these stringent requirements. Experiments and modeling establish that its temperature response depends only on well-known material constants of the silica FBG and the response of the probe laser’s wavelength controller. The response is independent of the linewidth of the slow-light resonance, hence different FBGs and/or resonances have the exact same response. Examples of measured cooling in optically pumped Yb-doped fibers show that more reliable thermal contact with the cooled fiber is obtained by wrapping the FBG and the cooled fiber.

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 score0.068
Threshold uncertainty score0.540

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 head0.017
GPT teacher head0.240
Teacher spread0.224 · 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

Citations3
Published2023
Admission routes1
Has abstractyes

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