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The<i>BRITE</i>Constellation Nanosatellite Mission: Testing, Commissioning, and Operations

2016· article· en· W2478790664 on OpenAlexaffabout

Bibliographic record

VenuePublications of the Astronomical Society of the Pacific · 2016
Typearticle
Languageen
FieldPhysics and Astronomy
TopicStellar, planetary, and galactic studies
Canadian institutionsUniversity of British ColumbiaRoyal Military College of CanadaUniversity of TorontoUniversité de Montréal
Fundersnot available
KeywordsPhotometry (optics)StarsSpace explorationSpace (punctuation)SatelliteOrbit (dynamics)Sensitivity (control systems)

Abstract

fetched live from OpenAlex

BRIght Target Explorer ( BRITE ) Constellation, the first nanosatellite mission applied to astrophysical research, is a collaboration among Austria, Canada and Poland. The fleet of satellites (6 launched; 5 functioning) performs precise optical photometry of the brightest stars in the night sky. A pioneering mission like BRITE —with optics and instruments restricted to small volume, mass and power in several nanosatellites, whose measurements must be coordinated in orbit—poses many unique challenges. We discuss the technical issues, including problems encountered during on-orbit commissioning (especially higher-than-expected sensitivity of the CCDs to particle radiation). We describe in detail how the BRITE team has mitigated these problems, and provide a complete overview of mission operations. This paper serves as a template for how to effectively plan, build and operate future low-cost niche-driven space astronomy missions.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0020.001
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.003

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 head0.017
GPT teacher head0.225
Teacher spread0.207 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations71
Published2016
Admission routes2
Has abstractyes

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