Crosschecking the Maunakea Spectroscopic Explorer performance budgets and science requirements compliance
Bibliographic record
Abstract
Maunakea Spectroscopic Explorer (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. Fibers at the prime focus will pick up the light and transmit it to banks of low/moderate (R=3,000/6,000) and high (R=40,000) resolution spectrographs. Actuators position individual fibers in the field of view to enable simultaneous full field coverage for both resolution modes. 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. A conceptual design was developed in recent years and the project is preparing for the preliminary design phase. Now is the time to do a thorough cross check of the system level performance budgets against the predicted performance of the conceptualized systems and to check their compliance against the high level science requirements. This is particularly important in light of changes in scope due to scientific revisions and to technical challenges encountered during the conceptual design phase. Areas of non-compliance will require review as to how best to mitigate the non-compliance. The results of this analysis led to issues being identified with the telescope and spectrograph concepts. This paper will summarize progress on this analysis, redesign, and trade study.
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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 | 0.059 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".