Raven: a harbinger of multi-object adaptive optics-based instruments at the Subaru Telescope
Bibliographic record
Abstract
In the context of instrumentation for Extremely Large Telescopes (ELTs), an Integral Field Spectrographs (IFSs), fed with a Multi-Object Adaptive Optics (MOAO) system, has many scientific and technical advantages. Integrated with an ELT, a MOAO system will allow the simultaneous observation of up to 20 targets in a several arc-minute field-of-view, each target being viewed with unprecedented sensitivity and resolution. However, before building a MOAO instrument for an ELT, several critical issues, such as open-loop control and calibration, must be solved. The Adaptive Optics Laboratory of the University of Victoria, in collaboration with the Herzberg Institute of Astrophysics, the Subaru telescope and two industrial partners, is starting the construction of a MOAO pathfinder, called Raven. The goal of Raven is two-fold: first, Raven has to demonstrate that MOAO technical challenges can be solved and implemented reliably for routine on-sky observations. Secondly, Raven must demonstrate that reliable science can be delivered with multiplexed AO systems. In order to achieve these goals, the Raven science channels will be coupled to the Subaru's spectrograph (IRCS) on the infrared Nasmyth platform. This paper will present the status of the project, including the conceptual instrument design and a discussion of the science program.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".