GHOST instrument control software: a progress report
Bibliographic record
Abstract
The Gemini High Resolution Optical Spectrograph (GHOST) is a dual-object integral-field unit fed echelle spectrograph currently under construction by an Australian-led consortium including the Australian National University (ANU), the Australian Astronomical Observatory (AAO) and Canada’s Herzberg Astronomy and Astrophysics Research Center. The instrument control software for GHOST is under development by ANU. A brief overview of the relevant instrument subsystems is presented from the point of view of instrument control, along with a high-level overview of the software design. We discuss the operational concepts that have required specific software solutions, including IFU positioner collision avoidance, focal plane image reconstruction, and the guiding loop. We provide details of the various screens in the Acceptance Test and Engineering User Interface, showing how they support the operational concepts. The project comprises a variety of software technologies, including the Gemini Instrument Application Programmer Interface (GIAPI), ANU CICADA software, and various off-the-shelf packages. We discuss the use of these technologies, and our experiences with using them. The various different hardware devices also require specific software support, and we discuss our experiences with vendor-supplied libraries and code. We conclude with a brief outline of the development process, together with a discussion of successes and challenges.
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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 | 0.029 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 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".