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Record W2795308348 · doi:10.5539/apr.v10n2p39

Cost Effective Profiling of Neodymium-Doped Vanadate Laser Pulses

2018· article· en· W2795308348 on OpenAlexvenueno aff
Raymond Edziah

Bibliographic record

VenueApplied Physics Research · 2018
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Fiber Laser Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsAutocorrelatorUltrashort pulseLaserOpticsMaterials scienceNeodymiumAutocorrelationMode-lockingOptoelectronicsComputer sciencePhysics

Abstract

fetched live from OpenAlex

As environmental conditions and component degradation and failure are known to affect the performance of ultrafast lasers, it is important to monitor their state in any nonlinear optical study. This may be achieved by measuring the temporal width of the laser pulses using an autocorrelator. In this work, an autocorrelator for measuring the pulsewidth of mode-locked lasers was custom-built using pieces of equipment usually found in a typical ultrafast optics laboratory. The assembled equipment was tested using SESAM (Saturable Semiconductor Absorber Mirror) mode-locked neodymium-doped vanadate (Nd:YVO4) laser having manufacturer specified average output power of 1.6 W and 10 ps pulsewidth. Using the background-free autocorrelation technique, the pulse width of the laser was measured to be 10.4 ps. This type of autocorrelator is cost effective and may be handy in situations where research funds are limited; a scenario commonly experienced in research laboratories of developing countries. Additionally, the processes involved in assembling the autocorrelator provide a useful learning experience for new researchers. The study also outlined the processes involved in modifying an existing autocorrelation setup in order to measure laser beam spot size; a useful parameter in nonlinear optical studies.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.625
Threshold uncertainty score0.738

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.060
GPT teacher head0.380
Teacher spread0.320 · 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.

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
Published2018
Admission routes1
Has abstractyes

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