Procedures for Constraining Robotic Fiber Positioning for Highly Multiplexed Spectroscopic Surveys: The Case of FPS for SDSS-V
Bibliographic record
Abstract
One crucial aspect of planning any large scale astronomical survey is constructing an observing strategy that maximizes reduced data quality. This is especially important for surveys that are rather heterogeneous and broad-ranging in their science goals. The Sloan Digital Sky Survey V (SDSS-V), which now utilizes the Focal Plane System (FPS) to robotically place fibers that feed the spectrographs, certainly meets these criteria. The addition of the FPS facilities an increase in survey efficiency, number of targets and target diversity, but also means the positions of fibers must be constrained to allow for simultaneous observations of sometimes competing programs. The constraints on the positions of the fibers are clearly driven by properties of the science targets e.g., the type of target, brightness of the target, position of the target relative to others in the field, etc. The parameters used to describe these constraints will also depend on the intended science goal of the observation, which will vary with the types of objects requested for the particular observation and the planned sky conditions for the observation. In this work, we detail the SDSS-V data collection scenarios, which consist of sets of parameters that serve as the framework for constraining fiber placements. The numerical values of these parameters were set based on either past experiences or from a series of new tests, which we describe in detail here. These parameters allow a survey like SDSS-V to be algorithmically planned to maximize the science output, while guaranteeing data quality throughout its operation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".