MSE: Instrumentation for a massively multiplexed spectroscopic survey facility
Bibliographic record
Abstract
MSE is a massively multiplexed spectroscopic survey facility that will replace the Canada-France-Hawaii-Telescope. This 11.25-m telescope, with its 1.5 square degrees field-of-view, will observe 4,332 astronomical targets in every pointing by using fibers to pick up the light at the prime focus w and transmitting it to banks of low/moderate (R=3,000/6,000) and high (R=30,000) resolution spectrographs. Piezo actuators position individual fibers in the field of view to enable simultaneous full field coverage for both resolution modes. A Calibration system ensures good quality and reliable raw data. This instrument suite, dedicated to large scale surveys, will enable MSE to collect a massive amount of data: equivalent to a full SDSS Legacy Survey every 7 weeks. Since 2018, MSE has made progress by refining the science cases, exploring design space for the instrumentation and understanding the limits of chosen telescope and instrument architecture to achieve the science cases. To improve performance and reduce risk, challenging conceptual designs for spectrographs have been reconsidered. As well, the science calibration plan and associated technical hardware system have been developed to a conceptual design level. This paper includes a discussion of the trades, design decisions and outstanding risks for the entire instrument suite with a focus on recent developments for the spectrographs and calibration system.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.011 |
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".