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Record W4394694738 · doi:10.1088/2515-7647/ad3d1b

Spectrally tunable phase-biased NALM mode-locked Yb:fiber laser with nJ-level pulse energy

2024· article· en· W4394694738 on OpenAlexafffund
Saeid Ebrahimzadeh, Sakib Adnan, Y. Li, Vito F. Pecile, Jakob Fellinger, Sarper Salman, Christoph M. Heyl, Ingmar Hartl, Oliver H. Heckl, Gil Porat

Bibliographic record

VenueJournal of Physics Photonics · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Fiber Laser Technologies
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaChristian Doppler ForschungsgesellschaftAustrian Science Fund
KeywordsOpticsFiber laserLaserMaterials sciencePulse (music)ScalingMode-lockingPhysicsDetector

Abstract

fetched live from OpenAlex

Abstract Applications of mode-locked fiber lasers benefit from robust and self-starting mode-locking, spectral tuning, high pulse energy and high average power. All-polarization-maintaining (PM) fiber lasers mode-locked with a phase-biased nonlinear amplifying loop mirror (NALM) have been shown to be very robust and reliably self-starting, and provide either spectral tuning or high pulse energy, but not both. We report on a simple method for concurrent spectral tuning and nanojoule-level pulse energy scaling of an all-PM phase-biased NALM mode-locked Yb:fiber laser, which we demonstrate over a 54 nm tuning range, reaching up to 1.67 nJ pulse energy and 126 mW average power. Unlike other laser configurations, our results show that net normal dispersion is not necessary or optimal for scaling the pulse energy of this type of mode-locked fiber laser.

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 score0.003

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 head0.015
GPT teacher head0.271
Teacher spread0.256 · 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

Explore more

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