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Record W2555392757 · doi:10.1364/optica.3.001308

Hollow-core-waveguide compression of multi-millijoule CEP-stable 32  μm pulses

2016· article· en· W2555392757 on OpenAlexafffund
Guangyu Fan, Tadas Balčiūnas, Tsuneto Kanai, Tobias Flöry, G. Andriukaitis, Bruno E. Schmidt, François Légaré, Andrius Baltuška

Bibliographic record

VenueOptica · 2016
Typearticle
Languageen
FieldPhysics and Astronomy
TopicLaser-Matter Interactions and Applications
Canadian institutionsInstitut National de la Recherche Scientifique
FundersNatural Sciences and Engineering Research Council of CanadaFonds de recherche du Québec – Nature et technologiesAustrian Science FundCanada Foundation for InnovationMarketing Science Institute
KeywordsMaterials sciencePulse compressionOpticsSelf-phase modulationDispersion (optics)Carrier-envelope phaseAmplifierNonlinear opticsCore (optical fiber)Envelope (radar)LaserOptoelectronicsPhysicsTelecommunications

Abstract

fetched live from OpenAlex

We present the extension of high-power pulse compression deeper into the challenging IR spectral range around 3.2 μm wavelength, where the effects of material absorption, dispersion, and free electron disturbance on nonlinear propagation become increasingly limiting parameters. 5 mJ, 80 fs pulses from a KTA parametric amplifier were spectrally broadened in a large-core hollow fiber with argon as the nonlinear medium. Subsequent compression through anomalous dispersion in CaF2 yielded 2.5 mJ close-to-transform-limited two-cycle pulses exhibiting a passively stabilized carrier envelope phase (CEP). Furthermore, we outline the feasibility of generating sub-two-cycle pulses with good spatial and temporal characteristics.

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 score0.004

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.030
GPT teacher head0.296
Teacher spread0.266 · 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

Citations87
Published2016
Admission routes2
Has abstractyes

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