A precise photometric ratio via laser excitation of the sodium layer – I. One-photon excitation using 342.78 nm light
Bibliographic record
Abstract
ABSTRACT The largest uncertainty on measurements of dark energy using type Ia supernovae (SNeIa) is presently due to systematics from photometry; specifically to the relative uncertainty on photometry as a function of wavelength in the optical spectrum. We show that a precise constraint on relative photometry between the visible and near-infrared can be achieved at upcoming survey telescopes, such as at the Vera C. Rubin Observatory, via a laser source tuned to the 342.78 nm vacuum excitation wavelength of neutral sodium atoms. Using a high-power laser, this excitation will produce an artificial star, which we term a ‘laser photometric ratio star’ (LPRS) of de-excitation light in the mesosphere at wavelengths in vacuum of 589.16, 589.76, 818.55, and 819.70 nm, with the sum of the numbers of 589.16 and 589.76 nm photons produced by this process equal to the sum of the numbers of 818.55 and 819.70 nm photons, establishing a precise calibration ratio between, for example, the r and $z$ filters of the LSST camera at the Rubin Observatory. This technique can thus provide a novel mechanism for establishing a spectrophotometric calibration ratio of unprecedented precision for upcoming telescopic observations across astronomy and atmospheric physics; thus greatly improving the performance of upcoming measurements of dark energy parameters using type SNeIa. The second paper of this pair describes an alternative technique to achieve a similar, but brighter, LPRS than the technique described in this paper, by using two lasers near resonances at 589.16 and 819.71 nm, rather than the single 342.78 nm on-resonance laser technique described in this paper.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".