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Record W4400695465 · doi:10.3389/fphy.2024.1428671

The effects of thermalization on the performance of passively Q-switched IC-OPOs

2024· article· en· W4400695465 on OpenAlexafffund
Josh Kneller, Saeed Salimian Rizi, Liam Flannigan, Chang‐Qing Xu

Bibliographic record

VenueFrontiers in Physics · 2024
Typearticle
Languageen
FieldEngineering
TopicSolid State Laser Technologies
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsOPOSCrystal (programming language)Saturable absorptionMaterials scienceLaserOpticsLithium triborateThermalisationNonlinear opticsExcited statePulse (music)Q-switchingPulse-width modulationAtomic physicsPhysicsPower (physics)Fiber laser

Abstract

fetched live from OpenAlex

In this paper, we report a new model for passively Q-switched intracavity optical parametric oscillators (IC-OPOs), using Cr:YAG as a Q-switch crystal. The model considers the effects of thermalization in the Nd:YAG laser crystal, excited state absorption in the Cr:YAG saturable absorber, nonlinear loss due to pump depletion in the nonlinear crystal, the thermal lens effect and the mode overlap of the fields within the nonlinear crystal. The calculated pulse width for the 1,064 nm pump light was 6.2 ns compared to the experimental result of 6 ns, while the signal wave was calculated to have a pulse width of 1.75 ns compared to the experimental result of 2 ns. The model is shown to accurately calculate both the temporal and output power characteristics of the IC-OPO, which has not been previously reported to the best of our knowledge.

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)0.001
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 score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.004
GPT teacher head0.186
Teacher spread0.182 · 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
Published2024
Admission routes2
Has abstractyes

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