Enhancement of laser-induced molecular alignment by simultaneous photodissociation
Bibliographic record
Abstract
From a three-dimensional time-dependent Schr\"odinger equation simulation for alignment of ${\mathrm{Cl}}_{2}$ by short laser pulses, we show that alignment of the ground state $(X{}^{1}{\ensuremath{\Sigma}}_{g}^{+})$ can be increased by photodissociation via a repulsive dissociative state ${(}^{1}{\ensuremath{\Pi}}_{u}).$ Initially, an intense nonresonant laser pulse ($2\ifmmode\times\else\texttimes\fi{}{10}^{13}{\mathrm{W}/\mathrm{c}\mathrm{m}}^{2},$ 1064 nm) with linear polarization aligns the molecule in the direction of the polarization as measured by $〈{\mathrm{cos}}^{2}\ensuremath{\theta}〉,$ which depends on the $J,$ ${M}_{J}$ initial conditions. Then, a second pulse with the same polarization is added in order to make perpendicular resonant transitions to the repulsive state $(1\ifmmode\times\else\texttimes\fi{}{10}^{13}{\mathrm{W}/\mathrm{c}\mathrm{m}}^{2},$ 375 nm). We show as an example for $J=4$ that the second pulse increases the alignment for each initial ${M}_{J}$ condition as measured by $〈{\mathrm{cos}}^{2}\ensuremath{\theta}{〉}_{{J=4,M}_{J}}$ and the average alignment $〈〈{\mathrm{cos}}^{2}\ensuremath{\theta}〉{〉}_{J=4}$ by reducing the proportion of the ${M}_{J}\ensuremath{\ne}0$ states. This improvement in the alignment of the molecule increases the amplitudes of the average rotational recurrences after the laser pulse and reduces the randomness in these recurrences by removing nonaligned ${M}_{J}\ensuremath{\ne}0$ states.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".