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Role of XUV Photons in Atomic High-Order Above-Threshold Ionization Processes in IR+XUV Two-Color Laser Fields<sup> * </sup>

2017· article· en· W2765339045 on OpenAlexafffund
Kui Zhang, Min Liu, Bingbing Wang, Yingchun Guo, Zong-Chao Yan, Jing Chen, Xiaojun Liu

Bibliographic record

VenueChinese Physics Letters · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicLaser-Matter Interactions and Applications
Canadian institutionsUniversity of New Brunswick
FundersNational Key Research and Development Program of ChinaNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsExtreme ultravioletPhysicsIonizationAtomic physicsLaserPhotonElectronUltravioletInfraredAbove threshold ionizationIonPhotoionizationOpticsNuclear physics

Abstract

fetched live from OpenAlex

We investigate the above-threshold ionization of an atom in a combined infrared (IR) and extreme ultraviolet (XUV) two-color laser field and focus on the role of XUV field in the high-order above-threshold ionization (HATI) process. It is demonstrated that, in stark contrast to previous studies, the XUV laser may play a significant role in atomic HATI process, and in particular, the XUV laser can accelerate the ionized electron in a quantized way during the collision between the electron and its parent ion. This process cannot be explained by the classical three-step model. Our results indicate that the previously well-established concept that HATI is an elastic recollision process is broken down .

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.006
GPT teacher head0.255
Teacher spread0.249 · 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 source (direct Gemma or distilled Codex), 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

Citations9
Published2017
Admission routes2
Has abstractyes

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