Maunakea Spectroscopic Explorer exposure time calculator for end-to-end simulator: to optimizing spectrograph design and observing simulation
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".