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Record W3104017913 · doi:10.1088/1367-2630/ab28b9

Hyper-Raman lines emission concomitant with high-order harmonic generation

2019· article· en· W3104017913 on OpenAlexaff
Étienne Bloch, Samuel Beaulieu, D. Descamps, S. Petit, François Légaré, A. I. Magunov, Y. Mairesse, V. V. Strelkov

Bibliographic record

VenueNew Journal of Physics · 2019
Typearticle
Languageen
FieldPhysics and Astronomy
TopicLaser-Matter Interactions and Applications
Canadian institutionsInstitut National de la Recherche Scientifique
FundersH2020 European Research CouncilRussian Science FoundationAgence Nationale de la Recherche
KeywordsPhysicsElectronLimit (mathematics)PhotonQuantum electrodynamicsQuantum mechanicsBound stateHarmonic oscillatorDirac equationWave functionDirac (video compression format)Uncertainty principleAtomic physicsMathematical analysisQuantum

Abstract

fetched live from OpenAlex

Abstract Hyper-Raman lines (HRL) resulting from strong-field light–matter interaction have been predicted theoretically in the 1990s but never identified in high-order harmonic generation experiments. Here, we use a combination of 800 and 400 nm laser pulses to control independently the two processes required for the hyper-Raman emission: creation of a coherence between two electronic states and laser-dressing of these states. As a result we observe simultaneously high-order harmonics, XUV free induction decay and HRL. We investigate experimentally and numerically the properties of this novel emission source. It can be of high interest, amongst others, for high-resolution spatio-temporal spectroscopy of excited electronic states in the same fashion high-order harmonics generation provides it for ground state.

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 score0.088
Threshold uncertainty score0.482

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 head0.016
GPT teacher head0.258
Teacher spread0.242 · 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
Published2019
Admission routes1
Has abstractyes

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