Nonexistence of a “local suppression” in the ionization of hydrogen atoms by a low-frequency laser field of arbitrary strength
Bibliographic record
Abstract
Further development of an analytical model for the tunneling ionization of atoms using a low-frequency laser field of arbitrary strength, including the strong-field region, is presented. The model uses a very accurate approximation of the true ionization barrier–potential in the Schrödinger equation by the effective parabolic barrier–potential based on the algorithm suggested by Miller and Good and later employed by Kulyagin and Taranukhin (KT) for calculating the ionization rate, W(F), of hydrogen atoms from the ground state by a linearly polarized laser field. We point out and eliminate a number of principal errors made by KT and calculate W(F) much more accurately. We demonstrate that the dependence of the ionization rate on the strength of the linearly polarized laser field is monotonic and does not show any effect of the stabilization (“local ionization suppression”) claimed by KT. Our results for W(F) are in good agreement with the results of quantum fully numerical simulations. The analytical method, further developed in the present paper, can be extended without difficulty to calculations of the tunneling ionization for a number of other quantum systems. The most immediate extensions are to hydrogen atoms in an elliptically polarized laser field and atoms described by the Thomas–Fermi model.
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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 | 0.001 | 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 | 0.001 | 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".