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Record W3199224259 · doi:10.1002/adpr.202100113

Raman Red‐Shift Compressor: A Simple Approach for Scaling the High Harmonic Generation Cut‐Off

2021· article· en· W3199224259 on OpenAlexafffund
Katherine Légaré, Reza Safaei, Guillaume Barrette, L. Arias, Philippe Lassonde, Heide Ibrahim, Boris Vodungbo, Emmanuelle Jal, J. Lüning, Nicolas Jaouen, Zhensheng Tao, Andrius Baltuška, François Légaré, Guangyu Fan

Bibliographic record

VenueAdvanced Photonics Research · 2021
Typearticle
Languageen
FieldPhysics and Astronomy
TopicLaser-Matter Interactions and Applications
Canadian institutionsInstitut National de la Recherche Scientifique
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of Canada
KeywordsHigh harmonic generationRaman scatteringPhotonLaserScalingPhoton energyRaman spectroscopyOpticsUltravioletWavelengthPhysicsMaterials scienceOptoelectronics

Abstract

fetched live from OpenAlex

The use of ultrashort laser pulses with long wavelengths as drivers is a relevant strategy for scaling high harmonic generation (HHG) to higher photon energies. Here, stimulated Raman scattering enhanced by the formation of multidimensional solitary states in a molecular gas‐filled hollow‐core fiber as the mechanism to produce a versatile HHG driver is reported on. This recently discovered method allows to red shift and to compress conventional subpicosecond laser pulses with a simple experimental apparatus, ultimately increasing the generated photon energy, while assuring a high photon flux. The adaptability, simplicity, and stability of this method make it attractive for tailoring HHG sources to individual applications at specific photon energies. Measurements of resonant magnetic scattering in a cobalt/platinum multilayer sample are presented as a demonstration of the relevance of this approach for photon‐hungry applications in the extreme ultraviolet.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.078
GPT teacher head0.388
Teacher spread0.310 · 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 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

Citations8
Published2021
Admission routes2
Has abstractyes

Explore more

Same venueAdvanced Photonics ResearchSame topicLaser-Matter Interactions and ApplicationsFrench-language works237,207