Visibility Predictions for Near-future Satellite Megaconstellations: Latitudes near 50° Will Experience the Worst Light Pollution
Bibliographic record
Abstract
Abstract Megaconstellations of thousands to tens of thousands of artificial satellites (satcons) are rapidly being developed and launched. These satcons will have negative consequences for observational astronomy research, and are poised to drastically interfere with naked-eye stargazing worldwide should mitigation efforts be unsuccessful. Here we provide predictions for the optical brightnesses and on-sky distributions of several satcons, including Starlink, OneWeb, Kuiper, and StarNet/GW, for a total of 65,000 satellites on their filed or predicted orbits. We develop a simple model of satellite reflectivity, which is calibrated using published Starlink observations. We use this model to estimate the visible magnitudes and on-sky distributions for these satellites as seen from different places on Earth, in different seasons, and different times of night. For latitudes near 50° north and south, satcon satellites make up a few percent of all visible point sources all night long near the summer solstice, as well as near sunrise and sunset on the equinoxes. Altering the satellites’ altitudes only changes the specific impacts of the problem. Without drastic reduction of the reflectivities, or significantly fewer total satellites in orbit, satcons will greatly change the night sky worldwide.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".