Calibration of a High-Resolution Slow-Light Fiber-Bragg-Grating Sensor for Temperature Measurements of Laser-Cooled Fibers
Bibliographic record
Abstract
Anti-Stokes fluorescence is emerging as an important new technique to eliminate the internal heat generated in rare-earth-doped fiber lasers and amplifiers. The efficiency of cooling is quantified by measuring the fiber temperature as a function of pump power launched in the fiber. Because the temperature changes induced in small-core fibers placed in air are small, these measurements require a sensor that can resolve millikelvin (mK) temperature changes. The accuracy is critical because they provide invaluable information about the degree of quenching and residual absorption in the fiber, which gauge the absolute merit of different core-glass compositions. This function has been well served by a closed-loop fiber sensor with mK resolution utilizing a slow-light fiber Bragg grating (FBG) placed parallel to and in contact with a stripped section of the cooled fiber. In this work, we report several improvements in the operation and calibration of this sensor. First, to obtain a more accurate and reproducible temperature reading, the contact between the two fibers is improved by twisting one fiber around the other. Second, careful calibration shows that the sensor response is independent of the slow-light resonance, of the FBG bandgap, and of the settings of the electronics, in agreement with a model. The response needs to be measured only once, then used for any FBG fabricated in the same type of fiber. Third, measurements are reported to quantify the temperature difference between the cooled fiber and the FBG, and how much the presence of the FBG affects the temperature of the cooled fiber, to determine the correction factor that must be applied to the sensor output to obtain accurate absolute readings. The sensor performance is illustrated with a measurement of cooling in a new Yb-doped aluminophosphosilicate fiber.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".