ScopeSim: a flexible general purpose astronomical instrument data simulation framework in Python
Bibliographic record
Abstract
ScopeSim is a flexible multipurpose instrument data simulation framework built in Python. It enables both raw and reduced observation data to be simulated for a wide range of telescopes and instruments quickly and efficiently on a personal computer. The software is currently being used to generate simulated raw input data for developing the data reduction pipelines for the MICADO and METIS instruments at the ELT. The ScopeSim environment consists of three main packages which are responsible for providing on-sky target templates (ScopeSim_templates), the data to build the optical models of various telescopes and instruments (instrument reference database), and the simulation engine (ScopeSim). This strict division of responsibilities allows ScopeSim to be used to simulate observation data for many different instrument and telescope configurations for both imaging and spectroscopic instruments. ScopeSim has been built to avoid redundant calculations where ever possible. As such it is able to deliver simulated observations on time scales of seconds to minutes. All the code and data is open source and hosted on Github. The community is also most welcome, and indeed encouraged to contribute to code ideas, target templates, and instrument packages.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.021 | 0.009 |
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".