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Electronic State Population Dynamics upon Ultrafast Strong Field Ionization and Fragmentation of Molecular Nitrogen

2022· article· en· W4295709204 on OpenAlexaff
Carlo Kleine, Marc‐Oliver Winghart, Zhuang-Yan Zhang, Maria Richter, Maria Ekimova, Sebastian Eckert, Marc J. J. Vrakking, Erik T. J. Nibbering, Arnaud Rouzée, Edward R. Grant

Bibliographic record

VenuePhysical Review Letters · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicLaser-Matter Interactions and Applications
Canadian institutionsUniversity of British Columbia
FundersH2020 Marie Skłodowska-Curie ActionsHorizon 2020 Framework ProgrammeAir Force Office of Scientific ResearchEuropean Commission
KeywordsAtomic physicsExcited stateIonizationLasing thresholdPopulationFragmentation (computing)Population inversionFilamentationPicosecondFemtosecondExcitationUltrashort pulseLaserMaterials sciencePhysicsIonOptics

Abstract

fetched live from OpenAlex

Air lasing from single ionized ${\mathrm{N}}_{2}^{+}$ molecules induced by laser filamentation in air has been intensively investigated and the mechanisms responsible for lasing are currently highly debated. We use ultrafast nitrogen $K$-edge spectroscopy to follow the strong field ionization and fragmentation dynamics of ${\mathrm{N}}_{2}$ upon interaction with an ultrashort 800 nm laser pulse. Using probe pulses generated by extreme high-order harmonic generation, we observe transitions indicative of the formation of the electronic ground $X^{2}{\mathrm{\ensuremath{\Sigma}}}_{g}^{+}$, first excited $A{^{2}\mathrm{\ensuremath{\Pi}}}_{u}$, and second excited $B^{2}{\mathrm{\ensuremath{\Sigma}}}_{u}^{+}$ states of ${\mathrm{N}}_{2}^{+}$ on femtosecond timescales, from which we can quantitatively determine the time-dependent electronic state population distribution dynamics of ${\mathrm{N}}_{2}^{+}$. Our results show a remarkably low population of the $A^{2}{\mathrm{\ensuremath{\Pi}}}_{u}$ state, and nearly equal populations of the $X^{2}{\mathrm{\ensuremath{\Sigma}}}_{g}^{+}$ and $B^{2}{\mathrm{\ensuremath{\Sigma}}}_{u}^{+}$ states. In addition, we observe fragmentation of ${\mathrm{N}}_{2}^{+}$ into N and ${\mathrm{N}}^{+}$ on a timescale of several tens of picoseconds that we assign to significant collisional dynamics in the plasma, resulting in dissociative excitation of ${\mathrm{N}}_{2}^{+}$.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.462
Threshold uncertainty score0.367

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 head0.267
Teacher spread0.262 · 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 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
Published2022
Admission routes1
Has abstractyes

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