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Record W2856702003 · doi:10.1117/12.2311952

Automated testing of optical fibres: towards the design of the Maunakea Spectroscopic Explorer Fibre Transmission System

2018· preprint· en· W2856702003 on OpenAlexafffund
Stephanie Monty, Farbod Jahandar, Jooyoung Lee, Kim A. Venn, Colin Bradley, Darren Erickson, D. Crampton, Victor Nicolov, Collin Kielty, Celine Mazoukh, Patrick B. Hall

Bibliographic record

VenueGround-based and Airborne Instrumentation for Astronomy VII · 2018
Typepreprint
Languageen
FieldPhysics and Astronomy
TopicAstronomy and Astrophysical Research
Canadian institutionsYork UniversityHerzberg Institute of AstrophysicsFiberTech Optica (Canada)University of Victoria
FundersNational Research Council CanadaNatural Sciences and Engineering Research Council of Canada
KeywordsAutomationCollimated lightTransmission (telecommunications)Optical fiberSoftwareComputer scienceTest benchMaterials scienceOpticsMechanical engineeringEngineeringLaserPhysicsTelecommunicationsEmbedded system

Abstract

fetched live from OpenAlex

We present the results of an automated fibre optic test bench constructed at the University of Victoria as part of the Maunakea Spectroscopic Explorer (MSE) Fibre Transmission System (FiTS). In preparation for MSE-FiTS, we have begun characterizing the focal ratio degradation (FRD) of candidate multi-mode fibres with the ultimate goal of testing all 4000 MSE fibres. To achieve this, we have built an optical bench to perform an automated version of the collimated beam test. Herein we present the design of the bench and discuss the automation of components by introducing the Big FiTS Fibre Wrapper (Big FFW), our open-source automation software. We conclude with the results of tests performed using the Big FFW on a sample of candidate fibre, comparing the Big FFW results against those found using manual methods. Our results suggest that the candidate MSE fibre meets the science requirement of < 5% FRD at f /2 and find less than 1% disagreement between both measurement methods.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.924
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.042
GPT teacher head0.290
Teacher spread0.248 · 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.

Study designOther design
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 routes2
Has abstractyes

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