Bibliographic record
Abstract
Adaptive optics systems using sodium laser guide stars are widely employed at major astronomical observatories. It is natural to ask whether other atomic species might offer advantages. In this paper, we review all abundant atoms and ions in the upper atmosphere, including Na, Fe, M g + , S i + , C a + , and K and also the non-metallic species N, N + , O, and H, considering their potential for adaptive optics. Return fluxes for all transitions that can be excited using either one or two wavelengths were computed. We also considered multi-wavelength emission, comparing the performance of different transitions for polychromatic laser guide star (PLGS) adaptive optics. We find that of all the mesospheric metals, Na is the most suitable for both monochromatic laser guide stars and PLGSs, providing about six times more return flux than the best transitions in Fe. For high-altitude observatories, excitation at 330 nm in Na should give the highest PLGS performance. Atomic O, N, and N + have strong transitions and very high abundances in the mesosphere. This makes them potential candidates for the generation of intense laser guide stars by amplified spontaneous emission, if a suitable excitation process can be demonstrated. Direct excitation by CW lasers is impractical, as all transitions from the ground state are beyond the atmospheric cutoff. Nevertheless, it may be possible using high-power pulsed lasers.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.024 | 0.009 |
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".