Direct measurement of laser cooling of Yb:YAG crystal at atmospheric pressure using a fiber Bragg grating
Bibliographic record
Abstract
Although Yb:YAG has been cooled in a vacuum environment1, we report for the first time an experimental demonstration of optical cooling at atmospheric pressure. A Yb:YAG crystal is supported on thin silica fibers, inside a matt-black chamber with air at atmospheric pressure, and pumped at 1029 nm in the pulsed and CW regimes. Direct measurement of the crystal surface temperature during pumping was made possible by using a low thermal-mass, transparent fiber Bragg grating (FBG) sensor. The FBG interrogation system has sufficient sensitivity to measure the background absorption of the sample to below 10-4 cm-1, and bulk cooling at a pump power as low as 17 mW. The dynamical measurement of the temperature allows the determination of the overall heat transfer coefficient of the sample in the air, of 22 W.m-2K-1. A temperature drop of 8.8 K from the chamber temperature is observed in the Yb:YAG crystal in air when pumped with 4.2 W at 1029 nm, close to 8.9 K observed in vacuum1. A background absorption αb = 2.9×10-4 cm-1 is estimated with a pump wavelength at 1550 nm. Simulations predict further cooling when the sample’s cross sectional area and the pump power are optimized, including absorption saturation effects. The choice of an efficient geometry, the use of a readily available temperature sensor in less controlled environments should simplify implementation of laser cooling systems and the development of commercial devices.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".