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Record W4281957558 · doi:10.1103/physreva.106.043106

Mapping the direction of electron ionization to phase delay between VUV and IR laser pulses

2022· article· en· W4281957558 on OpenAlexaff
M. E. Mountney, G. P. Katsoulis, Søren H. Møller, Kamalesh Jana, P. B. Corkum, A. Emmanouilidou

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
FundersArmy Research OfficeEngineering and Physical Sciences Research Council
KeywordsElectronPhysicsAtomic physicsIonizationDipoleLaserTransition dipole momentAttosecondUltrashort pulseOpticsQuantum mechanicsIon

Abstract

fetched live from OpenAlex

We theoretically demonstrate a one-to-one mapping between the direction of electron ionization and the phase delay between a linearly polarized vacuum ultraviolet (VUV) and a circular infrared (IR) laser pulse. To achieve this, we use an ultrashort VUV pulse that defines the moment in time and space when an above-threshold electron is released in the IR pulse. The electron can then be accelerated to high velocities escaping in a direction completely determined by the phase delay between the two pulses. The dipole matrix element to transition from an initial bound state of the ${\mathrm{N}}_{2}$ molecule, considered in this work, to the continuum is obtained using quantum-mechanical techniques that involve computing accurate continuum molecular states. Following release of the electron in the IR pulse, we evolve classical trajectories, neglecting the Coulomb potential and accounting for quantum interference, to compute the distribution of the direction and magnitude of the final electron momentum. The concept we theoretically develop can be implemented to produce nanoscale ring currents that generate large magnetic 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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.838
Threshold uncertainty score0.737

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 science0.0000.000
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 head0.020
GPT teacher head0.369
Teacher spread0.349 · 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 designNot applicable
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

Citations2
Published2022
Admission routes1
Has abstractyes

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