Using two light-pollution models to investigate artificial sky radiances at Canary Islands observatories
Bibliographic record
Abstract
Astronomical observations are increasingly limited by light pollution, which is a product of the over-illumination of the night sky. To predict both the angular distribution of scattered light and the ground-reaching radiative fluxes, a set of models has been introduced in recent decades. Two distinct numerical tools, MSNsRAu and ILLUMINA, are compared in this paper, with the aim of identifying their strengths and weaknesses. The numerical experiment comprises the simulation of spectral radiances in the region of the Canary Islands. In particular, the light fields near the Roque de los Muchachos and Teide observatories are computed under various turbidity conditions. It is shown that ILLUMINA has enhanced accuracy at low elevation angles. However, ILLUMINA is time-consuming because of the two scattering orders incorporated into the calculation scheme. Under low-turbidity conditions and for zenith angles smaller than 70° the two models agree well, and thus can be successfully applied to typical cloudless situations at the majority of observatories. MSNsRAu is well optimized for large-scale simulations. In particular, the grid size is adapted dynamically depending on the distance between a light source and a hypothetical observer. This enables rapid numerical modelling for large territories. MSNsRAu is also well suited for the mass modelling of night-sky radiances after ground-based light sources are hypothetically changed. This enables an optimum design of public lighting systems and a time-efficient evaluation of the optical effects related to different lamp spectra or different lamp distributions. ILLUMINA provides two diagnostic geographical maps to help local authorities concerned about light-pollution control. The first map allows the identification of the relative contribution of each ground element to the observed sky radiance at a given viewing angle, while the second map gives the sensitivity, basically saying how each ground element contributes per lumen installed.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".