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Ultrafast Resonant State Formation by the Coupling of Rydberg and Dark Autoionizing States

2023· article· en· W4321210362 on OpenAlexafffund
Mangaljit Singh, M. A. Fareed, V. A. Birulia, A. I. Magunov, Alexei N. Grum-Grzhimailo, Philippe Lassonde, Antoine Laramée, Romain Marcelino, Ramin Ghahri Shirinabadi, François Légaré, T. Ozaki, V. V. Strelkov

Bibliographic record

VenuePhysical Review Letters · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicLaser-Matter Interactions and Applications
Canadian institutionsInstitut National de la Recherche Scientifique
FundersNatural Sciences and Engineering Research Council of CanadaRussian Science Foundation
KeywordsUltrashort pulseAtomic physicsPhysicsRydberg formulaDark stateResonance (particle physics)Rydberg stateExtreme ultravioletSpectroscopyHigh harmonic generationLaserIonizationOpticsQuantum mechanics

Abstract

fetched live from OpenAlex

Studying the dynamics of dark states is challenging due to their inability to undergo single-photon emission or absorption. This challenge is made even more difficult for dark autoionizing states owing to their ultrashort lifetime of a few femtoseconds. High-order harmonic spectroscopy recently appeared as a novel method to probe the ultrafast dynamics of a single atomic or molecular state. Here, we demonstrate the emergence of a new type of ultrafast resonance state as a manifestation of coupling between Rydberg and a dark autoionizing state dressed by a laser photon. Through high-order harmonic generation, this resonance results in extreme ultraviolet light emission that is more than one order of magnitude stronger than for the off-resonance case. The induced resonance can be leveraged to study the dynamics of a single dark autoionizing state and the transient changes in the dynamics of real states due to their overlap with the virtual laser-dressed states. In addition, the present results allow the generation of coherent ultrafast extreme ultraviolet light for advanced ultrafast science applications.

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 score0.383
Threshold uncertainty score0.236

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 head0.013
GPT teacher head0.289
Teacher spread0.275 · 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

Citations21
Published2023
Admission routes2
Has abstractyes

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