Steaming ahead with laser cooling: new perspectives in materials engineering for photonics in all-oxide silica glass
Bibliographic record
Abstract
We recently demonstrated laser induced cooling in all oxide silica glass [1], a proof of principle of our materials engineering approach. This technique has the potential of significantly impacting silica photonics by not only improving laser cooling with the preferred rare earth ion, Yb3+, but for the first time, also with different rare earths. The higher rare earth concentration possible in silica without affecting its optical properties, indicates that new amplifiers and laser may be possible. This talk will review our engineering perspective to mitigating serious materials shortcoming in silica and elaborate what may be possible for new applications in photonics. 1. J. Thomas, T. Meyneng, N. Gregoroire, F. Monet, A. Tehranchi, D. Seletskiy, Y. Messaddeq, Raman Kashyap, “Laser Cooling of a Novel GAYY Glass at Atmospheric Pressure”, Advanced Photonics Congress, Maastricht, Holland, Post Deadline paper JTH4A.5, Optica (28 July 2022).
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".