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Record W3013345201 · doi:10.1088/1361-6455/ab82e0

Selection of the magnetic quantum number in resonant ionization of neon using an XUV–IR two-color laser field

2020· article· en· W3013345201 on OpenAlexaff
Serguei Patchkovskii, Marc J. J. Vrakking, D. M. Villeneuve, Hiromichi Niikura

Bibliographic record

VenueJournal of Physics B Atomic Molecular and Optical Physics · 2020
Typearticle
Languageen
FieldPhysics and Astronomy
TopicLaser-Matter Interactions and Applications
Canadian institutionsJoint Attosecond Science LaboratoryUniversity of Ottawa
FundersJapan Society for the Promotion of Science
KeywordsPhotoionizationAtomic physicsIonizationNeonExtreme ultravioletPhysicsExcitationLaserPhoton energyPhotoelectric effectPhotonIonOpticsArgon

Abstract

fetched live from OpenAlex

Abstract A recent experiment (Villeneuve et al 2017 Science 356 1150) has shown that two-color photoionization of neon by the combination of an attosecond XUV pulse train and a moderately strong, linearly polarized IR pulse can preferentially produce photoelectrons with orbital angular quantum number l = 3 (f-wave) and magnetic quantum number m = 0. This result was rationalized by the occurrence of different Stark shifts of m = 0 and | m | = 1 sub-levels in the IR laser field. Here we perform 3D time-dependent Schrödinger equation calculations with a neon effective potential to identify the mechanism for the selective excitation and ionization of m = 0 sub-levels. Calculations of the ionization and excitation yields as a function of the IR intensity and the XUV and IR photon energy reveal that a coupling between two dominant ionization channels involving 3p and 3d intermediate excitations is responsible for the observed m -level selectivity. We compare calculated and measured photoelectron velocity map images and ionization yields over a range of IR intensities and XUV and IR photon energies, and confirm that the m = 0 or | m | = 1 channel, and thus a single set of the quantum numbers, ( J ion , l , m ), can be selected by an appropriate choice of these parameters.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.218
Threshold uncertainty score0.323

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 head0.012
GPT teacher head0.269
Teacher spread0.258 · 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

Citations10
Published2020
Admission routes1
Has abstractyes

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