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97% Throughput Hollow-Core Fibers - for Pulse Compression of high Power Yb Lasers

2023· article· en· W4388040047 on OpenAlexaff
Young‐Gyun Jeong, Alexis Labranche, Maksym Ivanov, Riccardo Piccoli, Luca Zanotto, G. Tempea, Pedram Abdolghader, Roberto Morandotti, François Légaré, Luca Razzari, Bruno E. Schmidt

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicLaser-Plasma Interactions and Diagnostics
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsLaserMaterials sciencePulse compressionCompression (physics)Pulse (music)OpticsCore (optical fiber)Fiber laserPulse durationPower (physics)Transmission (telecommunications)RangingYtterbiumOptoelectronicsComputer sciencePhysicsTelecommunications

Abstract

fetched live from OpenAlex

We demonstrate Yb laser (170fs) propagation in large a core hollow-core fiber (HCF) with 97.4% transmission and subsequent pulse compression down to 25fs. The tunable pulse duration between 25fs to 100fs can be used to enhance THz emission of BNA organic crystals for instance. We also demonstrate average power scaling of this approach to 270W paired with pulse compression of a 1.3ps Yb laser down to 100fs. A particular setup feature is its ability to tune the input energy and repetition rate, respectively, over an order of magnitude. Pulse compression is possible for input pulses energies ranging from 1mJ to 10mJ with the same setup.

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 score0.002
Threshold uncertainty score0.007

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.024
GPT teacher head0.289
Teacher spread0.265 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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