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Neutral-Ionic State Correlations in Strong-Field Molecular Ionization

2012· article· en· W2317574919 on OpenAlexaff
Marija Kotur, Congyi Zhou, Spiridoula Matsika, Serguei Patchkovskii, Michael Spanner, Thomas Weinacht

Bibliographic record

VenuePhysical Review Letters · 2012
Typearticle
Languageen
FieldPhysics and Astronomy
TopicLaser-Matter Interactions and Applications
Canadian institutionsSteacie Institute for Molecular Sciences
FundersU.S. Department of Energy
KeywordsIonizationPhysicsAtomic physicsElectronExcited stateIonic bondingSemiclassical physicsIonQuantum mechanicsQuantum

Abstract

fetched live from OpenAlex

We study correlations between neutral and ionic states in strong-field molecular ionization. We compare predictions based on Dyson orbital norms and quasistatic semiclassical tunneling theories (Keldysh and molecular orbital Ammosov-Delone-Krainov) with more detailed calculations of strong-field ionization which take into account (i) the Coulomb interaction between the outgoing continuum electron wave packet and the remaining bound electrons and (ii) electron-core interactions that cause distortions of the electronic continuum states during the ionization event. Our results highlight the prominence of electronic rearrangement effects in strong-field ionization with intense ultrafast laser pulses, where the outgoing continuum electron can cause electronic transitions in the parent ion. Calculations and measurements for excited uracil molecules reveal the breakdown of Keldysh-weighted Dyson norm predictions for ionization to different states of the molecular cation in the strong-field regime.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
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 head0.011
GPT teacher head0.300
Teacher spread0.289 · 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 designSimulation or modeling
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

Citations35
Published2012
Admission routes1
Has abstractyes

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