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Record W2050511134 · doi:10.1117/12.924926

Mitigation of transient meteor events in sodium layer by TMT NFIRAOS

2012· article· en· W2050511134 on OpenAlexafffund
Glen Herriot, Craig Irvin

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2012
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdaptive optics and wavefront sensing
Canadian institutionsHerzberg Institute of Astrophysics
FundersOntario Ministry of Research and InnovationBritish Columbia Knowledge Development FundNatural Sciences and Engineering Research Council of CanadaNational Astronomical Observatory of JapanDepartment of Science and Technology, Ministry of Science and Technology, IndiaUniversity of British ColumbiaOntario Ministry of Research, Innovation and ScienceCalifornia Institute of TechnologyGordon and Betty Moore Foundation
KeywordsMeteor (satellite)MeteoroidAltitude (triangle)LidarOpticsRemote sensingComputer scienceEnvironmental scienceGeodesyGeologyPhysicsMeteorologyMathematicsAstronomy

Abstract

fetched live from OpenAlex

NFIRAOS Small meteors usually bum up near the bottom of the sodium layer. Meteor trails can lead to temporary dra­ matic changes in the altitude of the sodium layer. This altitude change is very rapid, typically over 1 second, and after some unpredictable period of 10-20 seconds, can transition back to the nominal mean altitude also in about 1 second. The altitude change is very drastic and can jump by up to 1 km which, on the face of it, would cause 4 micrometers defocus errors on LGS WFS measurements for a 30-m telescope, unless properly tracked. Measurements by the UBC Lidar detected 20 meteor trails I hour, and of these, 1-2 are significant events. We report on a full end-to-end Simulink simulation for TMT NFIRAOS including: meteor events measured by the UBC Lidar; on-instrument NGS focus sensor running at 90 Hz (median sky coverage frame rate); optimal temporal blending with LGS WFS focus measurements; LGS WFS centroiding matched filter update and Truth WFS update very 3s; full trombone servo model including non-linear focus range vs stage position. We optimized our control architecture and traded off motor power dissipation versus residual wavefront error and Shack-Hartmann spot displacement and found range tracking errors induce 12 nm WFE in normal conditions and brief (Is) jumps of 30-80 nm WFE at the beginning and ending of meteor transients.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.326
Threshold uncertainty score0.873

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 head0.011
GPT teacher head0.229
Teacher spread0.218 · 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 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

Citations4
Published2012
Admission routes2
Has abstractyes

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