Aging tests for dielectric-coated aluminum to be used in the Sudbury Neutrino Observatory
Bibliographic record
Abstract
The Sudbury Neutrino Observatory is a heavy water Cerenkov detector designed to detect solar neutrinos. Its main objective is to confirm or negate the solar neutrino problem. To achieve maximum counting rates and, thus, minimum statistical uncertainties, collection of Cerenkov light must be maximized. Our group at UBC, working with collaborators at Oxford, have designed and tested an optical concentrator that couples with a photomultiplier tube to achieve an effective gain in light collection by nearly a factor of 2. We have designed a procedure for measuring the reflectivity of flat mirror immersed in water within the incident angular range of 15° to 750 to facilitate the reflectivity measurement of dielectric-coated aluminum (DCA) mirror—the reflective component in the the concentrators. DCA is a standard product used in lighting fixtures to enhance the total reflected light. We determined that our application would best be served with a dielectric coating that was 10% thinner than standard. Our DCA was manufactured with the thinner coating. To ensure that the mirror will not significantly deteriorate within the 10 year expected life span of the detector, equipment was designed and constructed that accelerates the aging of the mirror, allowing 10 year-equivalent aging to occur in 70 lab-days. The reflectivity of aged mirror was then measured to verify that no significant loss occured.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".