Gemini infrared multi-object spectrograph: instrument overview
Bibliographic record
Abstract
The Gemini Infrared Multi-Object Spectrograph (GIRMOS) is a powerful new instrument being built to facility- class standards for the Gemini telescope. It takes advantage of the latest developments in adaptive optics and integral field spectrographs. GIRMOS will carry out simultaneous high-angular-resolution, spatially-resolved infrared (1 - 2.4 µm) spectroscopy of four objects within a two-arcminute field-of-regard by taking advantage of multi-object adaptive optics. This capability does not currently exist anywhere in the world and therefore offers significant scientific gains over a very broad range of topics in astronomical research. For example, current programs for high redshift galaxies are pushing the limits of what is possible with infrared spectroscopy at 8 -10- meter class facilities by requiring up to several nights of observing time per target. Therefore, the observation of multiple objects simultaneously with adaptive optics is absolutely necessary to make effective use of telescope time and obtain statistically significant samples for high redshift science. With an expected commissioning date of 2023, GIRMOS’s capabilities will also make it a key followup instrument for the James Webb Space Telescope when it is launched in 2021, as well as a true scientific and technical pathfinder for future Thirty Meter Telescope (TMT) multi-object spectroscopic instrumentation. In this paper, we will present an overview of this instrument’s capabilities and overall architecture. We also highlight how this instrument lays the ground work for a future TMT early-light instrument.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.018 |
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".