The infrared imaging spectrograph (IRIS) for TMT: overview of innovative science programs
Bibliographic record
Abstract
IRIS (InfraRed Imaging Spectrograph) is a first light near-infrared diffraction limited imager and integral field spectrograph being designed for the future Thirty Meter Telescope (TMT). IRIS is optimized to perform astronomical studies across a significant fraction of cosmic time, from our Solar System to distant newly formed galaxies (Barton et al. [1]). We present a selection of the innovative science cases that are unique to IRIS in the era of upcoming space and ground-based telescopes. We focus on integral field spectroscopy of directly imaged exoplanet atmospheres, probing fundamental physics in the Galactic Center, measuring 104 to 1010 M supermassive black hole masses, resolved spectroscopy of young star-forming galaxies (1 < z < 5) and first light galaxies (6 < z < 12), and resolved spectroscopy of strong gravitational lensed sources to measure dark matter substructure. For each of these science cases we use the IRIS simulator (Wright et al. [2], Do et al. [3]) to explore IRIS capabilities. To highlight the unique IRIS capabilities, we also update the point and resolved source sensitivities for the integral field spectrograph (IFS) in all five broadband filters (Z, Y, J, H, K) for the finest spatial scale of 0.004" per spaxel. We briefly discuss future development plans for the data reduction pipeline and quicklook software for the IRIS instrument suite.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.016 |
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".