Laser energy-pooling processes in an optically thick Cs vapor near a dissipative surface
Bibliographic record
Abstract
We characterize, for the first time to our knowledge, the laser-induced backward fluorescence (retrofluorescence) spectra that result from energy-pooling collisions between Cs atoms near a dissipative thin Cs layer on a glass substrate. We resolve, experimentally and theoretically, the laser spectroscopic problem of energy-pooling processes related to the nature of the glass–metallic vapor interface. Our study focused on the integrated laser-induced retrofluorescence spectra for the 455.5-nm (72P3/2–62S1/2) and 852.2-nm (62P3/2–62S1/2) lines as a function of laser scanning through pumping resonance at the 852.2-nm line. We experimentally investigate the retrofluorescence from 420 to 930 nm, induced by a diode laser tuned either in the wings or in the center of the pumping resonance line. We present a detailed theoretical model of the retrofluorescence signal based on the radiative transfer equation, taking into account the evanescent wave of the excited atomic dipole strongly coupled with a dissipative surface. Based on theoretical and experimental results, we evaluate the effective nonradiative transfer rate A¯62P3/2→62S1/2sf for atoms in the excited 62P3/2 level located in the near-field region of the surface of the cell. Values extracted from the energy-pooling process analysis are equivalent to those found directly from the 852.2-nm resonance retrofluorescence line. We show that the effective energy-pooling coefficients k˜72P3/2 and k˜72P1/2 are approximately equal. The agreement between theory and experiment is remarkably good, considering the simplicity of the model.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".