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Record W2059663004 · doi:10.1103/physreva.85.023402

Frequency-domain theory of nonsequential double ionization in intense laser fields based on nonperturbative QED

2012· article· en· W2059663004 on OpenAlexaff
Bingbing Wang, Yingchun Guo, Jing Chen, Zong-Chao Yan, Panming Fu

Bibliographic record

VenuePhysical Review A · 2012
Typearticle
Languageen
FieldPhysics and Astronomy
TopicLaser-Matter Interactions and Applications
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsPhysicsIonizationAtomic physicsDouble ionizationPhotonAtom (system on chip)CollisionLaserElectronQuantum mechanicsIon

Abstract

fetched live from OpenAlex

We study nonsequential double ionization (NSDI) processes of an atom by applying the frequency-domain theory based on the nonperturbative quantum electrodynamics. We obtain the transition formulas that describe the NSDI processes caused by the collision ionization (CI) and the collision-excitation ionization (CEI) mechanisms. By analyzing the NSDI results of each above-threshold ionization (ATI) channel, we investigate the contributions to the NSDI from the backward and forward collisions. In particular, for the CI process, the backward collision makes a major contribution to the NSDI probability, whereas for the CEI process, it depends on the characteristics of the laser-atom system: if the energy that the recolliding electron needs to excite a bound electron is much larger than the laser photon energy, such as for the case of helium in this work, the backward collision dominates the contribution; otherwise, the forward collision dominates the contribution. We also discuss the source of interference fringes in the NSDI momentum spectra due to the CI mechanism and find that the fringes can be predicted by using a simple cosine function. This work can be regarded as a development of the frequency-domain theory, which may shed light on the study of multiparticle dynamics in intense laser fields.

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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.261
Threshold uncertainty score0.775

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)0.0010.000

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 head0.018
GPT teacher head0.310
Teacher spread0.292 · 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 designTheoretical or conceptual
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

Citations36
Published2012
Admission routes1
Has abstractyes

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