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Record W6987713742

Two-Colour Nonlinear Amplification for Mid-infrared Generation by Difference Frequency Generation

2024· dissertation· en· W6987713742 on OpenAlexaff

Bibliographic record

VenueUWSpace (University of Waterloo) · 2024
Typedissertation
Languageen
FieldEngineering
TopicPhotonic Crystal and Fiber Optics
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsLaserBroadbandWavelengthAmplifierChirped pulse amplificationDispersion (optics)Nonlinear systemFiber laserLaser power scalingPulse (music)
DOInot available

Abstract

fetched live from OpenAlex

In this thesis, I discuss the development of a two-colour laser system which uses an all-normal dispersion fiber laser in combination with a gain-managed nonlinear amplifier as its broadband source. The goal is to develop a two-colour laser which can be used for mid-infrared generation through difference frequency generation. Most molecules contain characteristic absorption features in the mid-infrared ”molecular fingerprint” region from 3 - 20 μm. Our goal is to create a high power laser that can access as much of these wavelengths as possible. This requires a wide wavelength separation between colours, along with broad bandwidths in each colour, while attaining high pulse energies. This work shows the use of nonlinear amplification of a two-colour seed in an Yb:fiber in order to combat gain narrowing and maintain broad wavelengths in the two colours through self-phase modulation. Many different experimental setups were implemented, which will be presented, with the most successful results being achieved with the use of a large-mode- area Yb:fiber. We demonstrate a two-colour laser source with colours centered at 1070 nm and 1120 nm with individual bandwidths of 30 nm and 21 nm respectively. This two-colour spectrum was amplified up to a total power of 6.76 W, with a repetition rate of 9.67 MHz, corresponding to a total pulse energy of 700 nJ.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.805
Threshold uncertainty score1.000

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 head0.014
GPT teacher head0.205
Teacher spread0.191 · 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.

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
Published2024
Admission routes1
Has abstractyes

Explore more

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