The Infrared Imaging Spectrograph (IRIS) for TMT: the science case
Bibliographic record
Abstract
The InfraRed Imaging Spectrograph (IRIS) is a first-light instrument being designed for the Thirty Meter Telescope (TMT). IRIS is a combination of an imager that will cover a 16. 4 field of view at the diffraction limit of TMT (4 mas sampling), and an integral field unit spectrograph that will sample objects at 4-50 mas scales. IRIS will open up new areas of observational parameter space, allowing major progress in diverse fields of astronomy. We present the science case and resulting requirements for the performance of IRIS. Ultimately, the spectrograph will enable very well-resolved and sensitive studies of the kinematics and internal chemical abundances of high-redshift galaxies, shedding light on many scenarios for the evolution of galaxies at early times. With unprecedented imaging and spectroscopy of exoplanets, IRIS will allow detailed exploration of a range of planetary systems that are inaccessible with current technology. By revealing details about resolved stellar populations in nearby galaxies, it will directly probe the formation of systems like our own Milky Way. Because it will be possible to directly characterize the stellar initial mass function in many environments and in galaxies outside of the the Milky Way, IRIS will enable a greater understanding of whether stars form differently in diverse conditions. IRIS will reveal detailed kinematics in the centers of low-mass galaxies, allowing a test of black hole formation scenarios. Finally, it will revolutionize the characterization of reionization and the first galaxies to form in the universe.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.025 | 0.017 |
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".