MétaCan
Menu
Back to cohort
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 OpenAlexafffund
M. H. Griessmann, Aldo C. Martinez-Becerril, Jeff S. Lundeen

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.

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)0.001
metaresearch head score (Gemma)0.003
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: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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

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
GenreMethods

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

Citations4
Published2023
Admission routes2
Has abstractyes

Explore more

Same venueOptics ContinuumSame topicAdvanced Optical Sensing TechnologiesFrench-language works237,207