Development of a near-infrared wide-field integral field unit by ultra-precision diamond cutting
Bibliographic record
Abstract
Integral field spectroscopy (IFS) is an observational method for obtaining spatially resolved spectra over a specific field of view (FoV) in a single exposure. In recent years, near-infrared IFS has gained importance in observing objects with strong dust attenuation or at a high redshift. One limitation of existing near-infrared IFS instruments is their relatively small FoV, less than 100 arcsec2, compared with optical instruments. Therefore, we developed a near-infrared (0.9 to 2.5 μm) image-slicer type integral field unit (IFU) with a larger FoV of 13.5×10.4 arcsec2 by matching a slice width to a typical seeing size of 0.4 arcsec. The IFU has a compact optical design utilizing off-axis ellipsoidal mirrors to reduce aberrations. Complex optical elements were fabricated using an ultra-precision cutting machine to achieve root mean square surface roughness of less than 10 nm and a P-V shape error of less than 300 nm. The ultra-precision machining can also simplify the alignment procedures. The on-sky performance evaluation confirmed that the image quality and the throughput of the IFU were as designed. In conclusion, we successfully developed a compact IFU utilizing an ultra-precision cutting technique, almost fulfilling the requirements.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".