The infrared imaging spectrograph (IRIS) for TMT: sensitivities and simulations
Bibliographic record
Abstract
We present sensitivity estimates for point and resolved astronomical sources for the current design of the InfraRed Imaging Spectrograph (IRIS) on the future Thirty Meter Telescope (TMT). IRIS, with TMT's adaptive optics system, will achieve unprecedented point source sensitivities in the near-infrared (0.84 - 2.45 μm) when compared to systems on current 8-10m ground based telescopes. The IRIS imager, in 5 hours of total integration, will be able to perform a few percent photometry on 26 - 29 magnitude (AB) point sources in the near-infrared broadband filters (Z, Y, J, H, K). The integral field spectrograph, with a range of scales and filters, will achieve good signal-to-noise on 22 - 26 magnitude (AB) point sources with a spectral resolution of R=4,000 in 5 hours of total integration time. We also present simulated 3D IRIS data of resolved high-redshift star forming galaxies (1 < z < 5), illustrating the extraordinary potential of this instrument to probe the dynamics, assembly, and chemical abundances of galaxies in the early universe. With its finest spatial scales, IRIS will be able to study luminous, massive, high-redshift star forming galaxies (star formation rates ~ 10 - 100 M_Θ yr^(-1)) at ~100 pc resolution. Utilizing the coarsest spatial scales, IRIS will be able to observe fainter, less massive high-redshift galaxies, with integrated star formation rates less than 1 MΘsensitivity compared to current integral field spectrographs. The combination of both fine and coarse spatial scales with the diffraction-limit of the TMT will significantly advance our understanding of early galaxy formation processes and their subsequent evolution into presentday galaxies.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".