Construction, testing, and commissioning of the SDSS-V Local Volume Mapper telescope system
Bibliographic record
Abstract
This paper presents an update on the construction, testing, and commissioning of the SDSS-V Local Volume Mapper (LVM) telescope system. LVM is one of three surveys that form the fifth generation of the Sloan Digital Sky Survey, and it will employ a coordinated network of four, 16-cm telescopes feeding three fiber spectrographs at the Las Campanas Observatory. The goal is to spectrally map approximately 2500 square degrees of the Galactic plane with 37” spatial resolution and R~4000 spectral resolution over the wavelength range 360-980 nm. LVM will also target the Magellanic Clouds and other Local Group galaxies. Each of the four LVM telescopes consists of a two-mirror siderostat in alt-alt configuration feeding an optical breadboard. This produces a fixed, stable focal plane for the fiber-based Integral Field Unit (IFU). One telescope hosts the science IFU, while two others observe adjacent fields to calibrate geocoronal emission. The fourth telescope makes rapid observations of bright stars to compensate telluric absorption. The entrance slits of the spectrographs intersperse the fibers from all three types of telescope, producing truly simultaneous science and calibration exposures. We summarize the final design of the telescope system and report on its construction, alignment and testing in the laboratory. We also describe our deployment plan for commissioning at LCO, anticipated for late 2022.
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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 | 0.013 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".