OBSERVING STRATEGY FOR THE SDSS-IV/MaNGA IFU GALAXY SURVEY
Bibliographic record
Abstract
Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) is an integral-field spectroscopic survey that is one of three core programs in the fourth-generation Sloan Digital Sky Survey (SDSS-IV). MaNGA's 17 pluggable optical fiber-bundle integral field units (IFUs) will observe a sample of 10,000 nearby galaxies distributed throughout the SDSS imaging footprint (focusing particularly on the North Galactic Cap). In each pointing these IFUs are deployed across a 3° field; they yield spectral coverage 3600−10300 Å at a typical resolution R ∼ 2000, and sample the sky with 2'' diameter fiber apertures with a total bundle fill factor of 56%. Observing over such a large field and range of wavelengths is particularly challenging for obtaining uniform and integral spatial coverage and resolution at all wavelengths and across each entire fiber array. Data quality is affected by the IFU construction technique, chromatic and field differential refraction, the adopted dithering strategy, and many other effects. We use numerical simulations to constrain the hardware design and observing strategy for the survey with the aim of ensuring consistent data quality that meets the survey science requirements while permitting maximum observational flexibility. We find that MaNGA science goals are best achieved with IFUs composed of a regular hexagonal grid of optical fibers with rms displacement of 5 μ m or less from their nominal packing position; this goal is met by the MaNGA hardware, which achieves 3 μ m rms fiber placement. We further show that MaNGA observations are best obtained in sets of three 15 minute exposures dithered along the vertices of a 1.44 arcsec equilateral triangle; these sets form the minimum observational unit, and are repeated as needed to achieve a combined signal-to-noise ratio of 5 Å −1 per fiber in the r -band continuum at a surface brightness of 23 AB arcsec −2 . In order to ensure uniform coverage and delivered image quality, we require that the exposures in a given set be obtained within a 60 minute interval of each other in hour angle, and that all exposures be obtained at airmass (i.e., within 1–3 hr of transit depending on the declination of a given field).
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".