THE MANGA INTEGRAL FIELD UNIT FIBER FEED SYSTEM FOR THE SLOAN 2.5 M TELESCOPE
Bibliographic record
Abstract
We describe the design, manufacture, and performance of bare-fiber integral field units (IFUs) for the SDSS-IV survey Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) on the the Sloan 2.5 m telescope at Apache Point Observatory. MaNGA is a luminosity-selected integral-field spectroscopic survey of 10 4 local galaxies covering 360–1030 nm at . The IFUs have hexagonal dense packing of fibers with packing regularity of 3 μ m (rms), and throughput of 96 ± 0.5% from 350 nm to 1 μ m in the lab. Their sizes range from 19 to 127 fibers (3–7 hexagonal layers) using Polymicro FBP 120:132:150 μ m core:clad:buffer fibers to reach a fill fraction of 56%. High throughput (and low focal-ratio degradation (FRD)) is achieved by maintaining the fiber cladding and buffer intact, ensuring excellent surface polish, and applying a multi-layer anti-reflection (AR) coating of the input and output surfaces. In operations on-sky, the IFUs show only an additional 2.3% FRD-related variability in throughput despite repeated mechanical stressing during plate plugging (however other losses are present). The IFUs achieve on-sky throughput 5% above the single-fiber feeds used in SDSS-III/BOSS, attributable to equivalent performance compared to single fibers and additional gains from the AR coating. The manufacturing process is geared toward mass-production of high-multiplex systems. The low-stress process involves a precision ferrule with a hexagonal inner shape designed to lead inserted fibers to settle in a dense hexagonal pattern. The ferrule ID is tapered at progressively shallower angles toward its tip and the final 2 mm are straight and only a few microns larger than necessary to hold the desired number of fibers. Our IFU manufacturing process scales easily to accommodate other fiber sizes and can produce IFUs with substantially larger fiber counts. To assure quality, automated testing in a simple and inexpensive system enables complete characterization of throughput and fiber metrology. Future applications include larger IFUs, higher fill factors with stripped buffer, de-cladding, and lenslet coupling.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.008 |
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".