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Record W7011760150

Multilayer Coatings for METIS Instrument

2010· article· en· W7011760150 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicHistory of Computing Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsOrbiterCoronagraphOptical coatingTelescopeSounding rocketCoatingSpectrographStray light
DOInot available

Abstract

fetched live from OpenAlex

The Multi Element Telescope for Imaging and Spectroscopy (METIS) is a coronagraph onboard of Solar\nOrbiter. It will perform simultaneous observations at HeII Lyman-α line, HI Lyman-α line and in visible. To\nachieve such capability, instrument mirrors need to be coated by multilayer (ML) structures with high efficiency\nat all three spectral ranges. Coatings with higher performances with respect standard Mo/Si are desirable. An\ninstrument prototype of METIS has just flown onboard of a NASA sounding rocket: in this case, optics were\ncoated with Mg/SiC MLs. Better performances have been obtained in terms of reflectivity, but long term\nstability of this coating is an open problem. Moreover the harsh conditions of the environment met during the\nSolar Orbiter mission given by plasma particles and high temperature could affect the lifetime of the optical\ncomponents on the long term. We present the design and reflectivity tests of multilayer structures in which\nperformances improvement is obtained by the use of novel capping layers. All multilayers are tuned at 30.4nm\nline but the design also maximize the performances at 121.6nm and 500 – 650 nm visible range. Analysis of\nSolar Orbiter environment have been carried on in order to point out the main damaging sources for the\nnanostructures. Computer simulations with a devoted software have been performed to preliminary evaluation\nof the possible instabilities in multilayers. Experimental tests for investigating the effects of the thermal heating\nand particles bombardments in the reflectivity performances have been planned.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.656
Threshold uncertainty score0.286

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 science0.0010.000
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 head0.020
GPT teacher head0.250
Teacher spread0.230 · 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 designNot applicable
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".

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Citations0
Published2010
Admission routes1
Has abstractyes

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