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Record W4408362582 · doi:10.1038/s41467-025-57618-8

Orbital angular momentum control of strong-field ionization in atoms and molecules

2025· article· en· W4408362582 on OpenAlexaff
Jean-Luc Bégin, Ebrahim Karimi, P. B. Corkum, Thomas Brabec, V. R. Bhardwaj

Bibliographic record

VenueNature Communications · 2025
Typearticle
Languageen
FieldPhysics and Astronomy
TopicLaser-Matter Interactions and Applications
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsAngular momentumIonizationPhysicsAtomic physicsField desorptionField (mathematics)MoleculeClassical mechanicsQuantum mechanicsIon

Abstract

fetched live from OpenAlex

Tunnel ionization, the fundamental process in strong field physics and attosecond science, along with the subsequent electron dynamics are typically governed by the polarization and carrier envelope phase of the incident laser pulse. Moreover, most light-matter interactions involve Gaussian beams and rely primarily on dipole-active transitions. In this article, we reveal that Orbital Angular Momentum (OAM) carrying beams enable to control tunnel ionization in atoms and molecules. The ionization process is manipulated by the sign and value of the OAM and by displacing the phase singularity. We show that the helical phase and field gradients inherent in the higher-order multipole expansion of the tunneling process cause ionization to depend on OAM. Simulations indicate that, in contrast to Gaussian beams, the ponderomotive effects can also be controlled with OAM and the asymmetry in the optical vortex. Our findings have an impact on attosecond science, spectroscopy, and super-resolution microscopy. Ionization of atoms and molecules is a ubiquitous phenomenon at the core of attosecond science, plasma, and strong-field physics. Here, the authors demonstrate that the orbital angular momentum of laser beams can be used to selectively control photoionization via asymmetrically displaced Laguerre-Gaussian beams, shedding light on the subtle role of spatial inhomogeneities.

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 score0.745
Threshold uncertainty score0.267

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 head0.005
GPT teacher head0.281
Teacher spread0.276 · 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
Published2025
Admission routes1
Has abstractyes

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