Intense-laser-field-enhanced ionization of two-electron molecules: Role of ionic states as doorway states
Bibliographic record
Abstract
We investigate the mechanism of enhanced ionization in two-electron molecules by solving exactly the time-dependent Schr\"odinger equation for a one-dimensional ${\mathrm{H}}_{2}$ in an ultrashort, intense $(I>~{10}^{14}{\mathrm{W}/\mathrm{c}\mathrm{m}}^{2})$ laser pulse $(\ensuremath{\lambda}=1064\mathrm{nm}).$ Enhanced ionization in two-electron systems differs from that in one-electron systems in that the excited ionic state ${\mathrm{H}}^{\mathrm{\ensuremath{-}}}{\mathrm{H}}^{+}$ regarded as the dominant doorway state to ionization crosses the covalent ground state HH in field-following time-dependent adiabatic energy. An analytic expression for the crossing condition obtained in terms of the lowest three states agrees with the numerical results. The gap at the avoided crossing decreases the initial covalent component and promotes electron transfer to ${\mathrm{H}}^{\mathrm{\ensuremath{-}}}{\mathrm{H}}^{+}.$ As the internuclear distance R decreases, the population of the ${\mathrm{H}}^{\mathrm{\ensuremath{-}}}{\mathrm{H}}^{+}$ created increases, whereas the ionization rate from a ${\mathrm{H}}^{\mathrm{\ensuremath{-}}}{\mathrm{H}}^{+}$ decreases owing to the stronger attraction by the distant nucleus. As a result, the rate has a peak at $R\ensuremath{\approx}6\mathrm{a}.\mathrm{u}.,$ where most adiabatic states avoid each other with considerable gaps.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".