Maunakea Spectroscopic Explorer exposure time calculator for end-to-end simulator: to optimizing spectrograph design and observing simulation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The Maunakea Spectroscopic Explorer (MSE) project will provide multi-object spectroscopy in the optical and near-infrared bands using an 11.25-m aperture telescope, repurposing the original Canada–France–Hawaii Telescope site. MSE will observe 4332 objects per single exposure with a field of view of 1.5 square degrees, utilizing two spectrographs with low-moderate (R∼3000, 6000) and high (R≈30,000) spectral resolution. In general, an exposure time calculator (ETC) is used to estimate the performance of an observing system by calculating the signal- to-noise ratio (S/N) and exposure time. We present the design of the MSE ETC, which has four calculation modes (S/N, exposure time, S/N trend with wavelength, and S/N trend with magnitude) and incorporates the MSE system requirements as specified in the conceptual design. The MSE ETC currently allows for user-defined inputs of the target AB magnitude, water vapor, air mass, and sky brightness AB magnitude (additional user inputs can be provided depending on the computational mode). The ETC is built using Python 3.7 and features a graphical user interface that allows for cross-platform use. The development process of the ETC software follows an Agile methodology and utilizes the unified modeling language diagrams to visualize the software architecture. We also describe the testing and verification of the MSE ETC.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it