Zirconia-titania-doped tantala optical coatings for low mechanical loss Bragg mirrors
Bibliographic record
Abstract
The noise caused by internal mechanical dissipation in high refractive index amorphous thin films in dielectric mirrors is an important limitation for gravitational wave detection. The objective of this study is to decrease this noise spectral density, which is linearly dependent on such dissipation and characterized by the loss angle of Young’s modulus, by adding zirconia to titania-doped tantala, from which the current mirrors for gravitational wave detection are made. The purpose of adding zirconia is to raise the crystallization temperature, which allows the material to be more relaxed by raising the practical annealing temperature. The Ta, Ti, and Zr oxides are deposited by reactive magnetron sputtering in an Ar:O2 atmosphere using radio frequency and high power impulse plasma excitation. We show that, thanks to zirconia, the crystallization temperature rises by more than 150°C, which allows one to obtain a loss angle of 2.5×10−4, that is, a decrease by a factor of 1.5 compared to the current mirror high-index layers. However, due to a difference in the coefficient of thermal expansion between the thin film and the silica substrate, cracks appear at high annealing temperature. In response, a silica capping layer is applied to increase the temperature of crack formation by 100°C.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".