Titania Mixed with Silica: A Low Thermal-Noise Coating Material for Gravitational-Wave Detectors
Bibliographic record
Abstract
Coating thermal noise is one of the dominant noise sources in current gravitational wave detectors and ultimately limits their ability to observe weaker or more distant astronomical sources. This Letter presents investigations of ${\mathrm{TiO}}_{2}$ mixed with ${\mathrm{SiO}}_{2}$ (${\mathrm{TiO}}_{2}:{\mathrm{SiO}}_{2}$) as a coating material. We find that, after heat treatment for 100 h at $850\text{ }\ifmmode^\circ\else\textdegree\fi{}\mathrm{C}$, thermal noise of a highly reflective coating comprising of ${\mathrm{TiO}}_{2}:{\mathrm{SiO}}_{2}$ and ${\mathrm{SiO}}_{2}$ reduces to 76% of the current levels in the Advanced LIGO and Advanced Virgo detectors---with potential for reaching 45%, if we assume the mechanical loss of state-of-the-art ${\mathrm{SiO}}_{2}$ layers. Furthermore, those coatings show low optical absorption of $<1\text{ }\text{ }\mathrm{ppm}$ and optical scattering of $\ensuremath{\lesssim}5\text{ }\text{ }\mathrm{ppm}$. Notably, we still observe excellent optical and thermal noise performance following crystallization in the coatings. These results show the potential to meet the parameters required for the next upgrades of the Advanced LIGO and Advanced Virgo detectors.
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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) | 0.001 | 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".