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Record W2090757382 · doi:10.1364/ao.46.000533

Parasitic diffuse reflection in a Fourier transform spectrometer yielding subharmonic ghosts and line-shape distortion

2007· article· en· W2090757382 on OpenAlexaff
Geneviève Taurand, Jérôme Genest, Maxime Cadotte, M. Gibeault, É. Lanoue

Bibliographic record

VenueApplied Optics · 2007
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsOpticsPhysicsSpectrometerLaserFourier transform spectroscopyDistortion (music)InterferometryFourier transformPlane mirrorReflection (computer programming)Cardinal pointInfrared

Abstract

fetched live from OpenAlex

When using a high-resolution Fourier transform spectrometer (FTS) in a cube-corner configuration, subharmonic ghosts are observed in the spectrum. These ghosts are attributable to parasitic diffuse reflections on the mirrors of the FTS arm. The reflected beams skip a part of the interferometer and travel a different path from the main beam thus experiencing a smaller optomechanical gain. These reflections are present in the reference laser channel as well as on the measurement channel, and each affect the estimated spectrum differently. The sampling grid generated by the reference laser has periodic errors that are synchronized with the fringe signal. The measured spectrum can therefore exhibit sampling jitter ghosts at submultiples of the reference laser wavenumber in addition to its own additive subharmonics. The diffuse reflection experiencing the nominal optomechanical gain, such as in a plane-mirror configuration, will impact directly on the instrument line shape and on the radiometric accuracy of the spectrometer since some radiation is not propagating at the expected angles in the instrument.

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)0.003
metaresearch head score (Gemma)0.005
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 score0.003
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.002
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 head0.014
GPT teacher head0.243
Teacher spread0.229 · 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

Citations3
Published2007
Admission routes1
Has abstractyes

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