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Correlation of plasma ion densities and phase matching with the intensities of strong single high-order harmonics

2008· article· en· W2058454836 on OpenAlexaff
L-B Elouga-Bom, F. Bouzid, François Vidal, J. C. Kieffer, T. Ozaki

Bibliographic record

VenueJournal of Physics B Atomic Molecular and Optical Physics · 2008
Typearticle
Languageen
FieldPhysics and Astronomy
TopicLaser-Matter Interactions and Applications
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsHarmonicsPlasmaAtomic physicsIonHarmonicPhysicsPhase (matter)Intensity (physics)Monochromatic colorLaserChemistryOpticsVoltageQuantum mechanics

Abstract

fetched live from OpenAlex

We studied the correlation between the intensity of the strong quasi-monochromatic harmonics (13th harmonic of indium and 17th harmonic of tin) of an 800 nm laser pulse and the characteristics of the plasma in which the harmonics were generated. By varying the intensity of the prepulse that produces the plasma from a solid target, we found an optimum plasma condition under which the intensity of the strong harmonics reached maximum. Fluid simulations suggest that this behaviour is correlated to the maximum density of singly charged ions, which are thought to be responsible for the generation of the strong single harmonics via specific plasma resonances. This result is consistent with the model recently proposed by Milošević (2007 J. Phys. B: At. Mol. Opt. Phys. 40 3367). We also examined the alternative mechanism of phase matching between the harmonics and the pump laser to explain the role of plasma resonances in the enhancement effect. An optimum plasma condition is found as in the experiments although the relation between the strong single harmonic intensity and the singly charged ion density is not straightforward. The ac Stark broadening of the plasma resonances appears to be a critical parameter for phase matching to occur.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.541
Threshold uncertainty score0.350

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 head0.011
GPT teacher head0.235
Teacher spread0.225 · 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

Citations43
Published2008
Admission routes1
Has abstractyes

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