Automated testing of optical fibres: towards the design of the Maunakea Spectroscopic Explorer Fibre Transmission System
Bibliographic record
Abstract
We present the results of an automated fibre optic test bench constructed at the University of Victoria as part of the Maunakea Spectroscopic Explorer (MSE) Fibre Transmission System (FiTS). In preparation for MSE-FiTS, we have begun characterizing the focal ratio degradation (FRD) of candidate multi-mode fibres with the ultimate goal of testing all 4000 MSE fibres. To achieve this, we have built an optical bench to perform an automated version of the collimated beam test. Herein we present the design of the bench and discuss the automation of components by introducing the Big FiTS Fibre Wrapper (Big FFW), our open-source automation software. We conclude with the results of tests performed using the Big FFW on a sample of candidate fibre, comparing the Big FFW results against those found using manual methods. Our results suggest that the candidate MSE fibre meets the science requirement of < 5% FRD at f /2 and find less than 1% disagreement between both measurement methods.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".