The Infrared Imaging Spectrograph (IRIS) for TMT: advancing the data reduction system
Bibliographic record
Abstract
Infrared Imaging Spectrograph (IRIS) is the first light instrument for the Thirty Meter Telescope (TMT) that consists of a near-infrared (0.84 to 2.4 micron) imager and integral field spectrograph (IFS) which operates at the diffraction-limit utilizing the Narrow-Field Infrared Adaptive Optics System (NFIRAOS). The imager will have a 34 arcsec x 34 arcsec field of view with 4 milliarcsecond (mas) pixels. The IFS consists of a lenslet array and slicer, enabling four plate scales from 4 mas to 50 mas, multiple gratings and filters, which in turn will operate hundreds of individual modes. IRIS, operating in concert with NFIRAOS will pose many challenges for the data reduction system (DRS). Here we present the updated design of the real-time and post-processing DRS. The DRS will support two modes of operation of IRIS: (1) writing the raw readouts sent from the detectors and performing the sampling on all of the readouts for a given exposure to create a raw science frame; and (2) reduction of data from the imager, lenslet array and slicer IFS. IRIS is planning to save the raw readouts for a given exposure to enable sophisticated processing capabilities to the end users, such as the ability to remove individual poor seeing readouts to improve signal-to-noise, or from advanced knowledge of the point spread function (PSF). The readout processor (ROP) is a key part of the IRIS DRS design for writing and sampling of the raw readouts into a raw science frame, which will be passed to the TMT data archive. We discuss the use of sub-arrays on the imager detectors for saturation/persistence mitigation, on-detector guide windows, and fast readout science cases (< 1 second).
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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 | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.006 |
| Insufficient payload (model declined to judge) | 0.015 | 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".