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Record W4387771193 · doi:10.1117/12.2679002

Development of a 1U new space camera for EMCCD and CCD sensors with enhanced low-light sensitivity

2023· article· en· W4387771193 on OpenAlexaff
Olivier Daigle, Oleg Djazovski, J. Dupuis, René Doyon, Farbod Jahandar, Sarah Bouguéroua, Marie-Eve Ducharme, A. Gilbert, Jérémy Turcotte

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicCCD and CMOS Imaging Sensors
Canadian institutionsUniversité de MontréalCanadian Space Agency
Fundersnot available
KeywordsCoronagraphElectronicsSituation awarenessSensitivity (control systems)Computer scienceTelescopeRemote sensingImage sensorJames Webb Space TelescopeOrbital mechanicsOpticsPhysicsExoplanetArtificial intelligenceElectrical engineeringAerospace engineeringComputer visionEngineeringElectronic engineeringSatellite

Abstract

fetched live from OpenAlex

With recent advances in large-scale space telescope missions, new sensors and technologies are made available for use in space for the first time. With the recent developments for the Coronagraph Instrument (CGI) instrument of the Nancy Grace Roman Space Telescope (NGRST), Electron Multiplying CCD (EMCCD) readout electronics and sensors are being qualified for extended use in space. To make this new remote sensing technology available for a wider range of missions, a new space camera version has been developed, with the first units outfitted with the Teledyne-e2v CCD201-20 EMCCD sensor. This novel camera, equipped with proprietary Camera Proximity Electronics (CPE), is built with a balance of space-qualified components and commercial off the shelf components with flight heritage to optimize cost, performance, and reliability. In addition to direct imaging and characterization of exoplanets, the sensitivity of this camera is also enabling Space Situational Awareness applications. The first imaging, random vibration and TVAC testing results of this new 1U camera platform named nüSpace will be presented.

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 score0.035
Threshold uncertainty score0.498

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 head0.007
GPT teacher head0.204
Teacher spread0.197 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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