Measuring moving nuclear wave packets using laser Coulomb-explosion imaging
Bibliographic record
Abstract
From exact non-Born-Oppenheimer simulations of dynamics of ${\mathrm{H}}_{2}^{+}$ and ${\mathrm{D}}_{2}^{+}$ in an intense $(I>{10}^{15}{\mathrm{W}/\mathrm{c}\mathrm{m}}^{2})$ ultrashort laser pulse ${(t}_{p}<5\mathrm{fs}),$ we show that it is possible to measure the time evolution of the probability distribution $|\ensuremath{\psi}(R,t){|}^{2}$ of a dissociating wave packet using laser Coulomb-explosion imaging (LCEI). In our numerical simulation, a pump-probe technique is used: first, a weaker laser $(I<{10}^{14}{\mathrm{W}/\mathrm{c}\mathrm{m}}^{2})$ photodissociates the molecule and next, with experimentally controlled time delay ${t}_{0},$ a second intense $(I>{10}^{15}{\mathrm{W}/\mathrm{c}\mathrm{m}}^{2})$ ultrashort ${(t}_{p}<5\mathrm{fs})$ laser pulse is applied. The latter, ionizes ``instantaneously'' the dissociating molecule. Measurement of the kinetic-energy spectra of exploding nuclear fragments allows us, with the help of an inversion based on Coulomb's law and classical conservation of energy, to reconstruct the shape of the probability distribution $|\ensuremath{\psi}(R,t){|}^{2}$ at time ${t=t}_{0}.$ By repeating this experiment for many time delays ${t}_{0},$ one can thus obtain detailed information about the time-dependent dynamics of the photoinduced dissociation. The theory of LCEI for moving nuclear wave packets and the limitations of the above classical inversion method are presented.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Research integrity | 0.001 | 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".