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Record W4385191059 · doi:10.1364/optcon.494610

A method to determine the M<sup>2</sup> beam quality from the electric field in a single plane

2023· article· en· W4385191059 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueOptics Continuum · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Optical Sensing Technologies
Canadian institutionsUniversity of Ottawa
FundersCanada First Research Excellence FundNatural Sciences and Engineering Research Council of CanadaMitacsCanada Research Chairs
KeywordsElectric fieldOpticsTransverse planeLaser beam qualityBeam (structure)LaserPhysicsHolographyField (mathematics)Quality (philosophy)PhotonicsMeasure (data warehouse)Plane (geometry)Intensity (physics)Computational physicsLaser beamsComputer scienceEngineeringMathematics

Abstract

fetched live from OpenAlex

Laser beam quality is a key parameter for both industry and science. However, the most common measure, the M 2 parameter, requires numerous intensity spatial-profiles for its determination. This is particularly inconvenient for modelling the impact of photonic devices on M 2 , such as metalenses and thin-film stacks, since models typically output a single electric field spatial-profile. Such a profile is also commonly determined in experiments from e.g., Shack-Hartmann sensors, shear plates, or off-axis holography. We introduce and test the validity and limitations of an explicit method to calculate M 2 from a single electric field spatial-profile of the beam in any chosen transverse plane along the propagation direction.

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.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.796
Threshold uncertainty score0.409

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 studies0.0000.000
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 head0.030
GPT teacher head0.306
Teacher spread0.276 · 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