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Record W4285676393 · doi:10.1117/12.2630688

Tunable fibre Bragg grating arrays for spectral cross-correlation

2022· article· en· W4285676393 on OpenAlexaff
Polina Zavyalova, Suresh Sivanandam, Peter R. Herman, Ehsan Alimohammadian, Abdullah Rahnama

Bibliographic record

VenueGround-based and Airborne Instrumentation for Astronomy IX · 2022
Typearticle
Languageen
FieldEngineering
TopicAdvanced Fiber Optic Sensors
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsOpticsFiber Bragg gratingFemtosecondMaterials scienceLaserPhotonicsOptical fiberOptoelectronicsPhysics

Abstract

fetched live from OpenAlex

Miniaturization of astronomical instruments for high-resolution cross-correlation spectroscopy for exoplanet analysis may be achieved using existing photonic platforms and technologies. Arrays of fibre Bragg gratings (FBGs) may be inscribed into optical fibre to form a target-tailored instrument spectrum offering flexible modulation during instrument operation. This paper presents example spectra of the first such instrument designed for astronomical spectroscopy, fabricated by point-by-point writing with a femtosecond laser in the core of a single-mode telecommunication fibre. First results of spectral modulation and cross-correlation analysis are presented for a strain-modulated FBG array based on infrared molecular gas absorption.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.330
Threshold uncertainty score0.988

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.010
GPT teacher head0.236
Teacher spread0.226 · 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 designSimulation or modeling
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

Citations2
Published2022
Admission routes1
Has abstractyes

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