The Dipole Correction Method for Extracting Excited State Potentials and Electronic Transition Dipoles from Fluorescence Data
Bibliographic record
Abstract
Abstract We have developed a new inversion scheme for the accurate extraction of excited state potentials from fluorescence line positions and line strengths which does not make use of the Franck Condon Approximation (FCA). Our “dipole correction” method also enables the extraction of the coordinate dependence of the electronic transition dipoles. The accuracy of the potential energy surfaces (PES) thus extracted is much higher than that of the FCA‐ derived PES. The procedure, illustrated for the Na 2 A( 1 Σ + u ) → X( 1 Σ + g ) P‐branch emission, results in global errors of 0.1 cm −1 , and average errors near the PES minimum of 0.03 cm −1 , with A → X electronic transition dipole function accuracies better than 1×10 −3 Debye. We also show that it is possible to use emission data from a few select states: Global errors as small as 0.08 cm −1 for the Na 2 B( 1 Π u ) PES, using emissions data from only the s=0–5 low‐lying levels or the s=20–23 states, are demonstrated.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".