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Record W4398138449 · doi:10.1364/oe.523820

CPA-ready femtosecond pulses at 1 MHz from a custom recycled output Mamyshev oscillator

2024· article· en· W4398138449 on OpenAlexafffund
Vincent Boulanger, Michel Olivier, Alexandre Chevrette, François Trépanier, Michel Piché

Bibliographic record

VenueOptics Express · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Fiber Laser Technologies
Canadian institutionsTeraXion (Canada)Cégep Garneau
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of Canada
KeywordsFemtosecondOpticsMaterials scienceUltrafast opticsOptoelectronicsLaserPhysics

Abstract

fetched live from OpenAlex

A cost-effective fiber laser architecture is introduced in which the output seed pulse is stretched and then returned in the oscillator for an additional single-pass amplification without spectral broadening. It is implemented in an all-PM-fiber configuration based on a Mamyshev oscillator with a low repetition rate of 1 MHz. It features a linear oscillator bounded by two offset chirped fiber Bragg gratings accompanied by a third one acting as a pulse recycling filter. The latter tailors the pulse profile in amplitude and phase to seed femtosecond chirped-pulse amplification systems without additional pre-amplification nor pulse stretching. A single-pump prototype generating 200-nJ, 100-ps pulses compressible to 290 fs at 1030 nm and at 960 kHz is demonstrated. Furthermore, simulations show how this new oscillator architecture can provide tailored seed pulses with high enough spectral energy density and low enough nonlinear phase to generate sub-200 fs, 40 µJ, > 180 MW pulses from an all-fiber setup involving a single tapered-fiber power amplifier, without pulse picking.

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 score0.004
Threshold uncertainty score0.012

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.016
GPT teacher head0.255
Teacher spread0.239 · 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

Citations3
Published2024
Admission routes2
Has abstractyes

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