Hydrogen Concentration and Mechanical Dissipation upon Annealing in Zirconia-doped Tantala Thin Films for Gravitational Wave Observatory Mirrors
Bibliographic record
Abstract
Abstract Sensitivity in instruments such as the Laser Interferometer Gravitational-Wave Observatory (LIGO) is limited by a noise originating from fluctuations linked to internal mechanical dissipation (IMD) in the amorphous thin films of their Bragg reflectors. We investigate the correlation between IMD, characterized by a loss angle, and the hydrogen concentration in a thin film made of Zr-doped tantalum oxide after annealing at different temperatures. The film was deposited by magnetron sputtering and the loss angle measured by gentle nodal suspension. The concentration of heavier elements was obtained by Rutherford backscattering spectrometry (RBS). The hydrogen concentration in the as-deposited and annealed samples was obtained by elastic recoil detection (ERD). We observe that the hydrogen atomic concentration gradually decreases from 2.0 ± 0.1% down to the detection limit at 0.2 ± 0.1% as we anneal to 650°C. We also find that the loss angle decreases by a factor of two over the same annealing temperature range, suggesting that the loss angle is correlated with the hydrogen concentration. However, the loss angle remains relatively high even when most of the hydrogen is desorbed. We conclude that the presence of hydrogen is not the main limiting factor for further reducing the IMD.
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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".