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Record W2226810228 · doi:10.1103/physreva.76.033405

Effects of a static electric field on nonsequential double ionization

2007· article· en· W2226810228 on OpenAlexaff
Hongyun Li, Bingbing Wang, Jing Chen, Hongbing Jiang, Xiaofeng Li, Jie Liu, Qihuang Gong, Zong-Chao Yan, Panming Fu

Bibliographic record

VenuePhysical Review A · 2007
Typearticle
Languageen
FieldPhysics and Astronomy
TopicLaser-Matter Interactions and Applications
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsPhysicsElectric fieldAtomic physicsDouble ionizationIonizationKinetic energyElectronField (mathematics)Semiclassical physicsField desorptionLaserMomentum (technical analysis)IonQuantum mechanicsQuantum

Abstract

fetched live from OpenAlex

Using a three-dimensional semiclassical method, we perform a systematic analysis of the effects of an additional static electric field on nonsequential double ionization (NSDI) of a helium atom in an intense, linearly polarized laser field. It is found that the static electric field influences not only the ionization rate, but also the kinetic energy of the ionized electron returning to the parent ion, in such a way that, if the rate is increased, then the kinetic energy of the first returning electron is decreased, and vice versa. These two effects compete in NSDI. Since the effect of the static electric field on the ionization of the first electron plays a more crucial role in the competition, the symmetric double-peak structure of the ${\mathrm{He}}^{2+}$ momentum distribution parallel to the polarization of the laser field is destroyed. Furthermore, the contribution of the trajectories with multiple recollisions to the NSDI is also changed dramatically by the static electric field. As the static electric field increases, the trajectories with two recollisions, which start at the time when the laser and the static electric field are in the same direction, become increasingly important for the NSDI.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.726
Threshold uncertainty score0.248

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.008
GPT teacher head0.324
Teacher spread0.316 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations23
Published2007
Admission routes1
Has abstractyes

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