The Development of WIFIS: a Wide Integral Field Infrared Spectrograph
Bibliographic record
Abstract
We present the current results from the development of a wide integral field infrared spectrograph (WIFIS). WIFIS offers an unprecedented combination of etendue and spectral resolving power for seeing-limited, integral field observations in the 0.9 − 1.8 µm range and is most sensitive in the 0.9 − 1.35 µm range. Its optical design consists of front-end re-imaging optics, an all-reflective image slicer-type, integral field unit (IFU) called FISICA, and a long-slit grating spectrograph back-end that is coupled with a HAWAII 2RG focal plane array. The full wavelength range is achieved by selecting between two different gratings. By virtue of its re-imaging optics, the spectrograph is quite versatile and can be used at multiple telescopes. The size of its field-of-view is unrivalled by other similar spectrographs, offering a 4.5′′ × 12′ ′ integral field at a 10-meter class telescope (or 20′′ × 50′ ′ at a 2.3-meter telescope). The use of WIFIS will be crucial in astronomical problems which require wide-field, two-dimensional spectroscopy such as the study of merging galaxies at moderate redshift and nearby star/planet-forming regions and supernova remnants. We discuss the final optical design of WIFIS, and its predicted on-sky performance on two reference telescope platforms: the 2.3-m Steward Bok telescope and the 10.4-m Gran Telescopio Canarias. We also present the results from our laboratory characterization of FISICA. IFU properties such as magnification, field-mapping, and slit width along the entire slit length were measured
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".