Long-term monitoring of the throughput in Las Cumbres Observatory's fleet of telescopes
Bibliographic record
Abstract
The Las Cumbres Observatory operates a fleet of robotically controlled telescopes currently two 2m, nine 1m, and ten 0.4m telescopes, distributed amongst six sites covering both hemispheres. Telescopes of an aperture class are equipped with an identical set of optical imagers, and those data are subsequently processed by a common pipeline (BANZAI). The telescopes operate without direct human supervision, and assessing the daily and long-term scientific productivity of the fleet of telescopes and instruments poses an operational challenge. One key operational metric of a telescope/instrument system is throughput. We present a method of long-term performance monitoring based on nightly science observations: For every image taken in matching filters and within the footprint of the PANSTARRS DR1 catalog we derive a photometric zeropoint, which is a good proxy for system throughput. This dataset of over 250000 data points enables us to answer questions about general throughput degradation trends, and how individual telescopes perform at the various sites. This particular metric is useful to plan the effort level for on-site support and to prioritize the cleaning and re-aluminizing schedule of telescope optics and mirrors respectively.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".