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Record W3118618023 · doi:10.1364/ol.412296

70 mJ nonlinear compression and scaling route for an Yb amplifier using large-core hollow fibers

2021· article· en· W3118618023 on OpenAlexafffund
G. Fan, Paolo Carpeggiani, Zhensheng Tao, Giulio Coccia, Reza Safaei, Edgar Kaksis, A. Pugžlys, François Légaré, Bruno E. Schmidt, Andrius Baltuška

Bibliographic record

VenueOptics Letters · 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 CanadaNational Natural Science Foundation of ChinaCanada Foundation for InnovationAustrian Science FundAlexander von Humboldt-Stiftung
KeywordsOpticsMaterials scienceLaserRaman scatteringAmplifierFiber laserPulse compressionNonlinear opticsSelf-phase modulationFour-wave mixingWavelengthCore (optical fiber)OptoelectronicsRaman spectroscopyPhysicsTelecommunications

Abstract

fetched live from OpenAlex

In this Letter, we investigate the energy-scaling rules of hollow-core fiber (HCF)-based nonlinear pulse propagation and compression merged with high-energy Yb-laser technology, in a regime where the effects such as plasma disturbance, optical damages, and setup size become important limiting parameters. As a demonstration, 70 mJ 230 fs pulses from a high-energy Yb laser amplifier were compressed down to 40 mJ 25 fs by using a 2.8-m-long stretched HCF with a core diameter of 1 mm, resulting in a record peak power of 1.3 TW. This work presents a critical advance of a high-energy pulse (hundreds of mJ level) nonlinear interactions platform based on high energy sub-ps Yb technology with considerable applications, including driving intense THz, X-ray pulses, Wakefield acceleration, parametric wave mixing and ultraviolet generation, and tunable long-wavelength generation via enhanced Raman scattering.

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 score0.841
Threshold uncertainty score0.455

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 head0.042
GPT teacher head0.326
Teacher spread0.284 · 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

Citations62
Published2021
Admission routes2
Has abstractyes

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