The dissociation adiabaticity parameter and the strong field dissociation of HCl+
Bibliographic record
Abstract
In earlier work on H2+, we showed how a dissociation adiabaticity parameter, gammaDv identical with (Dv/2Upm)(1/2) (Dv is the dissociation energy from vibrational state v and U(pm) is the molecular ion system's ponderomotive energy), proposed by Walsh et al., can be modified and be a useful indicator of the strong field dissociation regime for a homonuclear diatomic. In the case of H2+, the new adiabaticity parameter, gamma(mol), indicates when a dissociation process can be most easily described as multiphoton above-threshold dissociation (gamma(mol)>1) and when it is better described using barrier-suppressed dissociation (gamma(mol)<1). In the case of a heteronuclear diatomic like HCl+, different electronic states can lead to different dissociation product channels to which are ascribed different gamma(mol) values. We show for a wide range of laser wavelengths and intensities that this adiabaticity parameter successfully predicts the type of dissociation dynamics (multiphoton above-threshold dissociation versus barrier-suppressed dissociation) on each electronic potential curve. We also discover that the dynamics in one electronic state can influence the dynamics in another at the same laser wavelengths and intensities, overriding the predictive capability of an adiabaticity parameter defined for a particular electronic state. Reasonable physical explanations are provided for these overriding cases.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".