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Record W4285043942 · doi:10.1103/physreva.106.013106

Disentangling interferences in the photoelectron momentum distribution from strong-field ionization

2022· article· en· W4285043942 on OpenAlexafffund
T. Wang, Z. Dube, Yonghao Mi, Giulio Vampa, D. M. Villeneuve, P. B. Corkum, XiaoJun Liu, A. Staudte

Bibliographic record

VenuePhysical review. A/Physical review, A · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicLaser-Matter Interactions and Applications
Canadian institutionsJoint Attosecond Science LaboratoryUniversity of Ottawa
FundersNational Research Council CanadaNatural Sciences and Engineering Research Council of CanadaDeutsche ForschungsgemeinschaftUniversity of Ottawa
KeywordsPhysicsMomentum (technical analysis)IonizationCoulombElectronAtomic physicsScatteringInterference (communication)Electric fieldSemiclassical physicsIonOpticsQuantum mechanicsChannel (broadcasting)TelecommunicationsQuantum

Abstract

fetched live from OpenAlex

Using the semiclassical two-step model for strong-field ionization, we theoretically investigate subcycle interference structures in the photoelectron momentum distribution. Specifically, we focus on the low-momentum fanlike interference structure. Employing a time-variable soft-core Coulomb potential, we demonstrate that the low-momentum interference arises from the interference between drifted and undrifted electrons from opposite direct quarter cycles. We also find that the main scattering in the nuclear Coulomb potential occurs just after ionization. Our findings suggest that the low-momentum region of the photoelectron spectrum is particularly sensitive to the ion potential and thereby offers another path to probe ultrafast electronic structure dynamics.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.424
Threshold uncertainty score0.831

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.013
GPT teacher head0.349
Teacher spread0.335 · 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

Citations9
Published2022
Admission routes2
Has abstractyes

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