Point spread functions for mapping artificial night sky luminance over large territories
Bibliographic record
Abstract
ABSTRACT Knowledge of the night sky radiance over a large region may be valuable information for identifying sites suitable for astronomical observations or for assessing the impact of artificial light at night on ecosystems. Measuring the sky radiance can be a complex endeavour, depending on the desired temporal and spatial resolution. Likewise, the modelling of artificial night sky radiance for multiple points of a region can represent a significant amount of computing time depending on the complexity of the model used. The use of the convolution of a point spread function with the geographical distribution of light sources has been suggested in order to model the sky radiance over large territories of hundreds of kilometres in size. We determined how the point spread function is sensitive to the main driving parameters of the artificial night sky radiance, such as the wavelength, the ground reflectance, the obstacle properties, the upward light output ratio, and the aerosol optical depth using the Illumina v2 model. The obtained functions were then used to model the artificial night sky brightness of the Mont-Mégantic International Dark Sky Reserve for winter and summer conditions. The results were compared with the New World Atlas of artificial night sky brightness, the Illumina v2 model, and in situ Sky Quality Camera measurements. We found that the New World Atlas overestimates the artificial sky brightness by 55 per cent, whereas the Illumina model underestimates it by 48 per cent. This may be due to varying atmospheric conditions and the fact that the model accounts only for public light sources.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".