Sky Quality Meter and satellite correlation for night cloud-cover analysis at astronomical sites
Bibliographic record
Abstract
ABSTRACT The analysis of night cloud cover is very important for astronomical observations in real time, considering a typical observation time of about 15 minutes, and to provide statistics. In this article, we use the Sky Quality Meter (SQM) for high-resolution temporal analysis of the La Silla and Asiago (Ekar Observatory) sky: 3 and 5 minutes respectively. We investigate the annual temporal evolution of the natural contributions of the sky at a site not influenced by artificial light at night (ALAN) and at one highly influenced. We also make a correlation between GOES and Aqua satellite data and ground-based SQM data to confirm the relationship between the SQM data and cloud cover. We develop an algorithm that allows the use of the SQM for night cloud detection and reach correlations with the nighttime cloud cover detected by the GOES and Aqua satellites of 97.2 per cent at La Silla and 94.6 per cent at Asiago. Our algorithm also classifies photometric (PN) and spectroscopic nights (SN). We measure 59.1 per cent PN and 21.7 per cent SN for a total percentage of clear nights of 80.8 per cent at La Silla in 2018. The respective Ekar Observatory values are 31.1 per cent PN, 24.0 per cent SN and 55.1 per cent of total clear night time. Application to the SQM network would involve the development of long-term statistics and large data forecasting models for site testing and real-time astronomical observation.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".