Phase Structure of Strong-Field Tunneling Wave Packets from Molecules
Bibliographic record
Abstract
We study the phase structure of the tunneling wave packets from strong-field ionization of molecules and present a molecular quantum-trajectory Monte Carlo model to describe the laser-driven dynamics of photoelectron momentum distributions of molecules. Using our model, we reproduce and explain the alignment-dependent molecular frame photoelectron spectra of strong-field tunneling ionization of N_{2} reported by M. Meckel et al. [Nat. Phys. 10, 594 (2014)]. In addition to modeling the low-energy photoelectron angular distributions quantitatively, we extract the phase structure of strong-field molecular tunneling wave packets, shedding light on its physical origin. The initial phase of the tunneling wave packets at the tunnel exit depends on both the initial transverse momentum distribution and the molecular internuclear distance. We further show that the ionizing molecular orbital has a critical effect on the initial phase of the tunneling wave packets. The phase structure of the photoelectron wave packet is a key ingredient for modeling strong-field molecular photoelectron holography, high-harmonic generation, and molecular orbital imaging.
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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 | 0.001 | 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) | 0.002 | 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".