The Infrared Imaging Spectrograph (IRIS) for TMT: final design development of the data reduction system
Bibliographic record
Abstract
IRIS (Infrared Imaging Spectrograph) is the near-infrared (0.84 to 2.4 micron) diffraction-limited imager and Integral Field Spectrograph (IFS) designed for the Thirty Meter Telescope (TMT) and the Narrow-Field Infrared Adaptive Optics System ( NFIRAOS ). The imager will have a 34 arcsec x 34 arcsec field of view with 4 milliarcseconds (mas) pixels. The IFS consists of a lenslet array and slicer, enabling four plate scales from 4 mas to 50 mas, with multiple gratings and filters. We will report the progress on the development of the IRIS Data Reduction System ( DRS ) in the final design phase. The IRIS DRS is being developed in Python with the software architecture based on the James Webb Space Telescope science calibration pipeline. We are developing a library of algorithms as individual Python classes that can be configured independently and bundled into pipelines. We will interface this with the observatory software to run online during observations and we will release the package publicly for scientists to develop custom analyses. It also includes a C library for readout processing to be used for both in real-time processing (e.g., up-the-ramp, MCDS) as well the ability for astronomers to use for offline reduction. Lastly, we will also discuss the development of the IRIS simulation packages that simulate raw spectra and image readout-data from the Hawaii-4RG detectors, which helps in developing reduction algorithms during this design phase.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.019 |
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".